The effects of data quality from the credit bureaus on the credit standing of the telecommunications companies in South Africa. By Vaneetha Govender (B.A. Management Leadership) (Honors Business Administration) Field Study Submitted in partial fulfilment of the degree MASTER’S IN BUSINESS ADMINISTRATION In the FACULTY OF ECONOMIC AND MANAGEMENT SCIENCES At the UNIVERSITY OF THE FREE STATE BLOEMFONTEIN Supervisor: Professor Helena van Zyl 2017 Table of Contents 1.2 PROBLEM STATEMENT 4 1.3 RESEARCH OBJECTIVES 5 1.4 PRELIMINARY LITERATURE REVIEW 5 1.4.1 External Credit Environment 6 Quality of the customer 7 Purpose of Credit Management 7 1.4.2 Function of the Credit Bureaus 7 1.4.3 Importance of data to the telecommunications companies 7 Weakness in the data 7 Credit Essential Data 8 Data Governance 8 1.4.4 Data Quality 9 Internal data impacts at the credit bureaus 9 1.4.5 Information Asymmetry 9 1.4.7 Role of the Credit Extender 10 1.4.6 Role of the credit bureau 10 1.4.8 External data Governance: The Credit Bureau Association 11 1.5 RESEARCH METHODOLOGY 11 1.5.1 Research Design 11 1.5.2 Sampling Design 12 1.5.3 Data Collection 13 The researcher will continue the interview process until no new themes emerge, and this is referred to as the data saturation point (Creswell & Lategan, 2003). 14 1.5.4 Data Analysis 15 1.5.5 Ethical Considerations 15 1.6 DEMARCATION OF THE STUDY 16 1.7 LAYOUT OF THE STUDY 16 1.8 CONCLUSION 17 REFERENCES 17 _______________ Chapter 1 _______________ Introduction 1.1 INTRODUCTION The research study will explore the impact that the quality of data received from the credit bureaus has on the key functions within the credit departments of the three major telecommunications companies in South Africa. This will include a focus on the effectiveness of credit management information used in the credit assessment process that is procured from the major credit bureaus in South Africa and moreover a SWOT analysis of the reliability of this information in relation to the products, processes and interventions that are utilised by telecommunications credit departments when making a credit decision. The overall aim of this study is to observe if these actions are robust enough to sustain the business performance or do they need to be refined in order to optimise the functioning of the credit risk portfolio of the business. Mobile Operators South Africa’s mobile market has high rates of penetration, according to 2015 statistics published by South Africa-based ICT consultancy World Wide Worx, which found that there were 80.2m sim cards in circulation and 42m mobile users in the country as of September 2014, a figure that amounts to 77.8% of the population. The country’s mobile market is dominated by five major GSM operators: Vodacom, a subsidiary of UK-based Vodafone; MTN South Africa, an arm of the publicly-listed South African company MTN Group; Telkom’s mobile subsidiary Telkom Mobile, Cell C; and Virgin Mobile. According to an April 2015 report in Business Tech, Vodacom is the largest player by market share, with 31.4m subscriptions, or 38.4% of South Africa’s estimated 81.7m. MTN SA is second, with 28m subscriptions, or 34.3% of the total, followed by Cell C, with 20m subscriptions; Telkom with 1.8m; and Virgin with 500,000. Telkom has since reported, however, that its mobile uptake rose 21.2% to hit 2.19m subsriptions as of June 2015. MTN Group was established in 1994 and is active in 21 markets across Africa and the Middle East, while Cell C was founded in November 2001 as a subsidiary of 3C Telecommunications, of which Oger Telecom South Africa owns 60%, CellSAF holds 25% and Lanun Securities SA has 15%. Vodacom was originally formed as a joint venture between Vodafone and Telkom, although the latter sold its 50% share in the company in 2008 – 15% to Vodafone and 35% to shareholders on the Johannesburg Stock Exchange – in order to reinvest in its own mobile unit. Vodacom is active in a number of African markets, including Tanzania, the Democratic Republic of Congo, Mozambique and Lesotho. Paraphrase and reference During 2008, the credit crisis experienced globally triggered questions on the quality of the credit risk analysis processes implemented by several financial intermediaries and telecommunications companys due to the credit losses they incurred pushing them close to telecommunications companyruptcy and at times into oblivion (Joseph, 2014). It has been argued by many experts that this could be the result of entities abandoning a sound analytical approach to credit extension or even the delusion that they are afforded a huge level of protection from credit tools and its derivatives. There is an increasing need for the telecommunications companies to design sound credit management that involves the identification of potential and existing risks inherent in extending credit facilities to a customer. The timely identification of potential credit default is important as high default rates result in increased bad debt and lowers revenue coming into the business, and can ultimately bring the organisation into financial distress (Baer, 2012). In contrast, lower credit exposure means a sound customer base with reduced chances of bad debt and therefore protects the organisation from financial distress in the looming scare of a recession and their constant challenges to remain profitable. A key component for determining credit exposure of the telecommunications industry is the information sourced from the leading credit bureau organisations in the country. These organisations gather and aggregate personal and financial worthiness as well as several sets of alternative credit information on individuals from data providers who are typically, creditors, lenders, debt collection agencies as well as other providers of utilities and services, and further information is sourced through agents from the national courts in the form of public records (Cibey, 2013). This data provided to the credit bureaus is then compiled into a customer record and made available on request to the customers of the credit bureau for the purpose of credit risk assessment or credit scoring. Credit scoring is a statistical basis for determining the likelihood an individual will repay his debt based on the findings and frequencies from other individuals in similar situations that have repaid their debt (Meissner, 2014). The logic underlying the existence of a credit bureau is to solve the problem of the information asymmetry between lenders and borrowers regarding the creditworthiness of the latter. Issuers with lower credit ratings pay higher interest rates embodying larger risk premiums than higher rated issuers (Japelli & Pagano, 2013). The most recent data from the National Credit Regulator (NCR) shows that South African consumer debt has increased to R1.63 trillion in the second quarter of 2015 (Reserve Telecommunications company, 2015). This situation is concerning as it highlights that although credit active customers honour their debt, approximately 45% are struggling to meet their debt repayments. According to data from a debt management firm, Debt Rescue, in August of 2015, consumers owed as much as three quarters (75%) of their monthly salaries to creditors and over 58% are struggling to pay off home loans and credit card debt. This has resulted in an increasing need for debt counselling initiatives, as well as a further degradation of the country’s economic standing. Looking at South Africa’s consumer debt from a global perspective, it amounts to $118 billion – this is larger than the total GDP output of smaller countries, such as Angola ($107 billion) and Morocco at ($130 billion). This debt amount is a third of South Africa’s GDP output, which pivots at around $350 billion (Katz et al, 2013). According to McKinsey (2014), “credit bureaus are essential in the financial infrastructures of the developed economies. For developing economies, credit bureaus are a sine qua non (a necessary condition, without which something is not possible) for expansion and growth. They allow increased access to credit, support responsible lending, reduce credit losses and strengthen telecommunications companying supervision capabilities in monitoring systemic risks.” Several legislative initiatives have been passed into South African law to protect consumer rights and to legislate lending in order to ensure fairness as well as the economic survival of the cash strapped consumer. The legislative initiative looming is the Treating Customers Fairly (TCF) bill which has not yet been promulgated, and will provide some level of protection for the consumer. The primary purpose of the TCF bill is two pronged as being both regulatory and supervisory in its approach, and is designed to ensure that very specific, soundly articulated outcomes regarding fairness in the procurement of financial services by consumers are delivered on a basis that is closely regulated. With the introduction of the Treating Customers Fairly (TCF) bill, there will be increased pressure on telecommunications companies to base credit decisions on accurate and valid information (Reserve Telecommunications company, 2015). The robustness of most economies is measured by the state of its financial services industry as it is one of the most closely regulated and critically important sectors in any economy (Getenga, 2012). The key credit functions performed by the telecommunications companies are very similar to that of the financial services industry, and therefore both share a key role in economic stability of the country. The telecommunications organisation contribute 12% to the South African GDP (Stats SA, 2016). Preliminary research indicates that the use of the credit bureau information in order to extend credit to consumers is an integrated and widely used process in all telecommunication and financial services companies in South Africa (Reserve Telecommunications company, 2015). They are cognisant that data quality is a huge problem in most organisations across several industries and poor quality data results in loss of money and increases bad debt write offs, which eventually affects the company’s revenue streams. The average annual bad debt write off across the three companies is 34% (Annual Reports 2016 Vodacom, MTN, CellC). Lang and Jagtiani (2010) express the sentiment that there is a requirement for further research into the practice of credit management as there is very limited studies that have been conducted in this regard. In light of this, the researcher will make a contribution to the body of knowledge in this area by conducting research into the credit management process in the telecommunications company with a particular focus on the quality of data used in the credit assessment process. 1.2 PROBLEM STATEMENT The problem is that the data utilised by the telecommunications companies obtained from the credit bureaus as part of credit decision making, may not be of the standard of quality expected by the credit team in support of sustainable credit performance for the organisation. If the problem is not addressed, the impact thereof is twofold. Firstly, it could result in potential loss of income for the company that could be detrimental to both its bad debt ratios and its credit reputation. Secondly, the impact on the individual consumer is also of concern, as the consumer could find him/her in a debt cycle that will take years to overcome; and the result is further degradation of consumer well-being and ultimately the state of the economy. 1.3 RESEARCH OBJECTIVES 1.3.1 Primary research objective The primary research objective of this study is to explore the effects of data quality from the credit bureaus on the credit standing of the telecommunications companies in South Africa. The following research questions are raised: • What are the perceptions of the credit officials of the impact the level of accuracy of the data has on the credit component of the telecommunication company? • What are the possible threats for telecommunications companies of poor governance around data put into the public domain by the credit bureaus? • What is the effect of asymmetry of information on the selection processes applied by the credit departments of the telecommunications companies? 1.3.2 Secondary research objectives The secondary research objectives of this study are to: • Discuss credit risk management; • Discern the use of credit bureau data in credit risk decisions; • Do a SWOT analysis on the credit risk decisions that is based on the credit bureau data; 1.4 PRELIMINARY LITERATURE REVIEW The credit extenders, being the telecommunications companies in this study have a significant dependency on the credit bureau data providers for its daily credit operations, where numerous decisions are taken each day to extend a credit facility or in most cases a physical handset or device to their customers based on the information provided. When a credit decision is made by the credit department of a telecommunications company, the primary concern is that information that may prove crucial in the credit assessment of the customer, may be omitted on that individual’s credit profile at the credit bureau. In a worst case it could be that incorrect, or derogatory information is placed on the customer’s profile. A secondary concern with the data, is that it is used to formulate the customer’s credit rating, and may be extensively used across many organisations and industries to assess the customer for many products and services. This scenario leads not only to the potential customer being biased, but can lead to a negative impact on organisational risk bearing. The function of the credit bureau is to collate data on both the credit and the payment behaviour of an individual’s financial accounts and more importantly, how these have been managed (McNab & Wynn, 2013). They will also provide other insights from information derived from the data that may be of interest to parties within the credit industry. This would include information about the individual’s home loan, telecommunications company accounts, debit cards, credit cards, loan accounts, and even insurance or medical bill accounts; it also extends to telecom and other personal utilities’ accounts. This set of information is then compiled with other data that is available in the public domain like, electoral rolls, high court judgements, sequestrations, debt rescue and voluntary payment agreements, as well as other private information that could be provided by companies that are part of the closed user group of the credit bureau organisation. This information is compiled into a credit bureau report which is utilised by several parties, numerous times over for credit decisions The effect of the problem is that errors in the credit bureau report and the ultimately the quality of information the used by the credit bureau to rate the risk a consumer would pose to a lending organisation, can result in the detriment of the consumer’s credit standing as well his/her financial well-being if he/she is overextended due to the poor quality of the products and services provided by the credit bureau; or in converse he/she could be refused any further credit due to misrepresentation of information. 1.4.1 External Credit Environment Credit management involves some components that are external to the business and some elements could at times be embedded in the data that will be utilised in the credit decision. These factors that are external to the organisation could influence the quality of the credit decision based on the premise that, “Many external factors do influence the results, actions and decisions of businesses, although diverse businesses are impacted by varied external factors in dissimilar ways” (Cibey, 2013). Quality of the customer The loan extended to a customer is based on the quality of the customer and his financial standing. Padilla and Pagano (1997) developed a model in which they premise that the performance of a loan depends on the quality of the borrower, called the Lenders’ Moral Hazard. At the initiation of the credit extension, each institution extending a credit facility possesses private information on the quality and credit standing of a borrower. According to Cibey (2013), a borrower can be denied or extended credit based on this private information and the borrower will be none the wiser as to the information utilised for the credit extension. Purpose of Credit Management Lang and Jagtiani (2010) cite that credit management is a system that is devised to prevent unwarranted damage to a firm or institution from unforeseen but probable events. In light of this premise the aim of the research is to identify key areas of the credit management process that is impacted by the quality of information sourced from the credit bureaus, which might need revision. 1.4.2 Function of the Credit Bureaus The hypothesis that information sharing enhances sound credit risk management is supported by a growing body of empirical evidence. Analyses of credit bureau data confirm that credit reporting reduces the selection costs of lenders by allowing them to more accurately predict individual loan defaults (Luoto et al, 2014). One of the features that telecommunications companys deliberate when deciding on a loan credit application is the estimated chances of recovery, according to Getenga (2012). In order to arrive at this decision, credit information is required on how well the applicant has conducted past loan agreements and the information is relevant as it indicates correlation between past and future performance on repayment behaviour. It is challenging for the telecommunications company to access a potential customer’s payment behaviour and credit records as it may be scattered over various institutions, therefore the credit bureau is crucial to the telecommunications company as it can provide to them a summarised view of all customer records. 1.4.3 Importance of data to the telecommunications companies Without data the modern commercial opportunities would be very limited. Data and information are fundamental to the success of any business today and they increasingly provide a commercial competitive edge (McNab & Wynn, 2013). Weakness in the data The quality of decisions made within the telecommunications companies is only as good as the information upon which they are based; it is unfortunate that a laxity exists about the data that is collected for customer decisions, which in many cases may be of poor quality, insufficient, deficient in correctness, or at best difficult to decipher. Poor quality intelligence has been the undoing of senior credit officials, companies, countries and CEOs, and others in high profile situations (Bitterer, 2011). Information is critical for sound credit functioning. Credit Essential Data The telecommunications companies need to continuously focus on the quality credit information, more specifically in terms of the following (Lehohla, 2011:7): • The traceability of the information; • Number of data sources; • The ease with which it can be summarised and analysed; • The amount of relevant information provided; and • Value added to decision making. Data Governance Data quality is defined by Lehohla (2011:7) in terms of the what is required as prerequisites for data quality as well as its seven crucial dimensions, namely relevance, accuracy, timeliness, accessibility, interpretability, coherence and integrity (Lehohla, 2011:7). Guba and Lincoln (2011) discuss the credit bureau as an institution with no direct dealings or relationships with consumers, proclaiming that it is largely unknown and misunderstood, maintaining large databases of information which may or may not be accurate. They concur that it has the power to determine whether or not an individual is given credit, buy a home, get insurance cover or even start a business and at its most influential even getting a job; it is the epitome of the remote database, in its size and potential for harm equaled only by the comprehensive records of taxation authorities, in South Africa, SARS. Figure 1.1 Data management cycle in a credit bureau Source: www.techtarget .com, 2013 1.4.4 Data Quality Data instability can arise from rapid growth or contraction in the bureau’s subscriber base determined by how many parties are contributing to the database of subscriber information. A change in the number of subscribers will affect the quality of the data; and a change in the nature of contributors will affect shared-performance data. In essence how much and who contributes can affect overall reliability and stability of the data (Anderson, 2010). Internal data impacts at the credit bureaus Anderson (2010) states a very important consideration for the users of information from the various credit bureaus, which highlights the fact that, companies who use the data from the bureaus are quite oblivious to the various internal changes in the contributors as well as the many drivers that directly impact the data quality. Due to the constant shift both the numbers and the types of contributors and subscribers to the credit bureaus, their data telecommunications companys are ever changing. These contributors and subscribers are ultimately the data owners from several organisations who create the information used by the telecommunications companies. 1.4.5 Information Asymmetry A credit bureau is considered an effective risk monitoring tool, which reduces the information gap between the telecommunications company and its existing as well as potential customers. The bureau’s primary purpose is to manage information asymmetry during a transaction, and it is perceived to help by eliminating this phenomenon during a transaction. It is most valuable where one party has more or better information than the other and the consolidated contributions of all parties positively impact information asymmetry (Lang & Jagtiani, 2010). 1.4.7 Role of the Credit Extender The data review cycle is crucial for credit management and is used as a means to identify credit failure and over indebtedness, it is also used extensively to mitigate the risks inherent in the credit processes where several external data elements are utilised. The credit data monitoring cycle can provide stability in the credit book against risks that may not be apparent when debt is extended to a customer (Aardt & Tonder, 2015). Figure 1.2 Steps in the credit monitoring cycle Source: www.techtarget .com 2016 The steps below are followed in a typical credit management cycle (Hillman, 2014): • Revise policies and procedures; • Identify, assess and prioritise risks; • Develop strategies to measure risks; • Design policies and procedures to mitigate risks; • Implement and assign responsibility; and • Test effectiveness and evaluate results. 1.4.6 Role of the credit bureau The means by which a credit bureau may assist an organisation in the extension of credit to potential customers are listed below. This study will utilise this list as a basis to glean truths and perceptions around this set of values (Anderson, 2010): • It is meant to promote responsible lending and borrowing by being the main catalyst in developing and adopting a sound credit culture; • It is said to provide credit insight to all lending institutions by enhancing their risk management practices; • Its assist lending institutions by mitigating their exposure to potential bad debts and NPLs; • It helps credit seeking borrowers to gain easier access to credit facilities; • It is perceived to positively impact the a company’s liquidity; • It can assist financial credit institutions to discover hidden market opportunities through significant data provision; and • It can stimulate and increase economic growth by accurately unlocking profitable opportunities for individuals. 1.4.8 External data Governance: The Credit Bureau Association The Credit Bureau Association (CBA) is the governing body for the credit bureaus and can control and legislate its information content. The CBA in turn is governed by the rules of the National Credit Act of 2014, and is an important feature on the credit lending landscape. There are ten or more credit bureaus operating in South Africa, with ITC and Experian dominating the market for consumer credit information. The major player for company data is KreditInform, while Compuscan is active in the micro-lending market. All of the credit bureaus are represented by the CBA, which has a Code of Conduct endorsed by the Business Practices Committee (White Paper 12 Feb 2015: The telecommunications companying association of South Africa, 2014). The code covers compliance procedures, disclosure of information to customers, procedures in the event of disputed accuracy, data retention periods, and disclosure to other parties. Even so, there is a public perception that the credit bureaus are perpetuating historical inequalities. This has been worsened, because the information provided has on occasion been inaccurate, and credit bureau data is sometimes used in employment screening. It is hoped that many of these perceptions will improve once the bureaus comply with the new NCA requirements (Hillman, 2014). 1.5 RESEARCH METHODOLOGY 1.5.1 Research Design An informed group of individuals will share their understanding of the extent to which poor data quality can impact both the individual consumer as well as the effective performance of a credit department when providing cellular contracts and telecommunications facilities to a new or existing customer. The nature of the research is explorative in nature and will be a qualitative study in order to fall within the researcher’s view of reality and were based on “a constructivist philosophy that assumes reality as multilayer, interactive, and a shared social experience interpreted by individuals understanding the social phenomena from the participant’s perspective (McMillan & Schumacher 2001:396). Exploratory research is usually conducted in order to determine the nature of the problem, and is not intended to provide conclusive evidence, but will assist the researcher to attain a better understanding of the problem that is being researched. As a result of the outcomes from this type of research, the researcher should be willing to change his/her direction as a result of revelation of new data and new insights (Research-methodology.net, Year?) A SWOT framework will be used in structuring data gathering, as well as the analysis of data for this study. This analytical and strategic planning tool was originally developed for strategic planning for marketing purposes in businesses but is now commonly used in the planning phase of various types of projects, including action research (Zuber-Skerritt 2002, 145). A SWOT analysis is regarded as a powerful tool for determining a project's capabilities (strengths) and deficiencies (weaknesses), its unexplored opportunities and the external threats to its long-term/future success (Schwalbe 2000,77; Thompson, Strickland & Gamble 2005,91).Startups.co.uk (n.d.) emphasises that a SWOT would not help to find a definite answer to all questions “but it will help you get your thoughts in order so you can concentrate on the main problem rather than a sea of problems”. In this SWOT analysis, attention will be given not only to the process of making credit decisions, but will include aspects (as outlined in the literature review) pertaining to the roles and responsibilities of credit bureaus and credit extenders; the quality of data used in the decision making; as well as external factors that can impact on the process. 1.5.2 Sampling Design The sampling technique that will be used for this study, will be non-probability, judgement sampling, which is also known as purposive sampling. It will allow the researcher to actively select the most productive sample of the population to answer the research question. (Cooper & Schindler, 2011) The data collection population of the study will consist of senior credit managers and executives within the telecommunications industry and its affiliates, and consists of the following credit officials at the top three telecommunications companies making the relevant population twenty one made up as follows for each of the institutions. Table 1 Key Credit staff for each Telecommunications Company Designation within the company Company A Company B Company C General Manager Credit X Credit Executive X X X Head of Credit Scoring and Risk X X X Senior Credit Manager X X X Credit Manager X X X Senior Credit Supervisor X X X Total 5 5 6 The credit portfolios for each of the telecommunications companies in South Africa has by way of a standard reporting structure, a Credit Chief or General Manager his direct report, who will be an Executive for Credit, a Senior Credit Manager and a Credit Manager, the study anticipates that all twenty one officials will be part of the study sample, and they will represent the top three telecommunications companies in South Africa, determined by their market share at the time of the study. The credit officials are members of the Credit Bureau Association and, they may make concrete contributions to the credit lending policy. 1.5.3 Data Collection The interviews will be semi-structured with all participants being asked the same basic set of questions and will take the form of a conversation with the intention that the questioning explores the participant’s views, ideas, beliefs and attitudes about certain events (Creswell & Lategan, 2003). The method of source triangulation will allow the researcher to examine the response consistency of different data sources from within the same method, for instance: • At different time intervals, separate points in time; • In public settings as opposed to private settings; and • Doing a comparison with people with different viewpoints. During the interview process, the interviewed candidates will share their views and opinions based on their own experiences and understandings, but the researcher will ensure that all aspects of the interviews are soundly monitored to ensure that information shared is trustworthy and credible. Guba and Lincoln (2011) cite credibility, transferability, dependability, and confirmability as being critical in the qualitative research paradigm. The meetings will be arranged timeously with the selected credit officials within the various telecommunication companies in order to provide ample time to explore meanings and expand of critical statements made by the officials during the interviews that bring deeper understanding to the study. The interviews will be recorded with the permission of each interviewee and they will sign such permission on a pre designed and populated sheet of approval, which will form part of the final interview pack. The meetings will be recorded on the researcher’s laptop, and these will later be transcribed, and the key repetitive themes will be extracted, making use of semi-structured interviews as a data gathering technique. This approach was chosen over structured interviews as they present an opportunity for the questions to be phrased appropriately, flexibility of the order of the questions posed, and also provides further opportunities to interject for clarity (Crouch & McKenzie, 2006). Individual, semi-structured face to face interviews with the credit officials as tabled above, will be undertaken with the credit officials during which the questions posed and the responses can be clarified and expounded on as is necessary. Respondents will be probed from a semi structured perspective. During the interview the researcher will act on opportunities to explore interrelationships between the concepts that will emerge from the interview and expand on research objectives that are of pertinent value to the study. The researcher will also be acutely aware of unexplored items that will be extracted from the interview and ask for further clarity and information. The interviews will be recorded, with the explicit permission of the candidates and the organisation where they are employed. The semi structured interviews will be based on a framework of the most pertinent variables that might influence the selected individual's contribution (Lategan, 2006). The study methodology will be based on the availability of relevant literature, the researcher's practical knowledge of the research area, and evidence that will emerge from the study. The respondents’ names will be kept confidential, and they will be chosen for the designated credit role they fulfil within the telecommunication companies. The researcher will continue the interview process until no new themes emerge, and this is referred to as the data saturation point (Creswell & Lategan, 2003). The interview process of the study will be conducted in the first quarter of 2017, in Buccleuch, Midrand Johannesburg or the designated offices of the senior officials of the telecommunications companies. The geographical area of the study will be the greater Gauteng area, where most of the offices are located. 1.5.4 Data Analysis The research methodology above details the research method to be qualitative and as such is the data analysis an ongoing, insightful and iterative or non-linear process. According to Tulip (2010) data analysis in qualitative research also refers to "reasoning and argumentation that is not based simply on statistical relations between 'variables', by which certain objects or observation units are described." Prior to proceeding with such an analysis, the information or voice recordings must be transcribed, which simply means that content of the interviews, the researcher’s side notes or memos are typed up into word documents. The next process is for these documents to be analysed using manual techniques or computer programmes, such as Atlas.ti. It is the intention of the researcher to utilise Atlas ti for purposes of disseminating the interview scripts into usable data elements, which will be used in the analysis process. These data elements will allow the researcher to draw commonalities and also to glean information from the data connections that will become possible during the iterative process of analysing the data. The data from the structured questions will be compiled into a Statistical Analysis System (SAS), and these responses will be analysed and linked to the informal interviews. Both sets of information will be combined in a seamless and sound way, in order to correlate the findings of the processes. 1.5.5 Ethical Considerations The two primary ethical considerations in any project are consent and confidentiality (Cooper & Schindler, 2011:139). Consent: All participants will be asked to voluntarily consent to their participation in the interview process and it will be made clear that they will not be coerced or put under any pressure during the interview. They will be informed in reasonable detail about the purpose of the study and what is required of them. Confidentiality: The identities of all participants will be protected at all stages of the process, as this will be communicated to all the participants in due course. Ethics is defined as what is deemed to be acceptable or unacceptable in human conduct (Tustin & Martins, 2005). It goes beyond the ambit of the law and into what is accepted by others as being good and right for all involved, e.g. of this being that the researcher must treat the responses from all the participants in the utmost confidence unless they have given explicit consent to be mentioned as having provided the information. For this research study the following standards were applied to ensure that all activities during the project are ethically sound: • Permission will be sought from the senior officials for the organisation in which the interview will be conducted. • All participants will be duly notified that their involvement in the interviews will not be compulsory but voluntary and at their own discretion. • All participants will be informed that their responses will be treated in the highest confidence, and they will be duly informed as such. 1.6 DEMARCATION OF THE STUDY The research study is conducted in the South African context and includes the perspectives of both customers of the credit bureaus as well as the impact on the consumers, to whom the customers of the credit bureau, extend credit facilities. The study will involve the executive and senior leadership of the credit departments in the three major telecommunication companies in South Africa. The credit executives and senior managers are key decision makers as to where and how bureau data is applied to a decision, and how the results are compiled into a final risk based facility that will be extended to a customer. The credit risk team members understand the impact of using the credit bureau data in a decision and they are also continually assessing the quality of the data as well as the performance of the decisions already taken. This team of individuals across the credit lending departments of the telecommunications institutions are therefore best placed to contribute to the study. The subject area is Credit Risk Management. 1.7 LAYOUT OF THE STUDY The study will follow the prescribed layout as detailed below: Chapter One – Introduction and problem statement. Chapter Two – Literature review Chapter Three – Research method Chapter Four – Discussion of findings Chapter Five - Recommendations and conclusion. 1.8 CONCLUSION This study will analyse how poor quality data from the credit bureaus can negatively impact the key functions of a credit department, these will be analysed and consolidated into a proposal of qualitative findings and will highlight further repercussions of using poor quality data from the credit bureaus when making credit management decisions. In this chapter a brief introduction to the study was presented. The researcher defined problems of data laxity, external factors, governance of data, and roles of the parties and the impact thereof in the telecommunications sector. The aims and objectives of the study was outlined, as well as the research methodology that will be employed. The chapter is concluded with an outline for the rest of the study. _______________ Chapter 2 _______________ Literature Review INTRODUCTION According to Saunders, Lewis and Thornhill (2004) a literature review is the framework upon which the research is based and it assists with developing a sound understanding and insight into emerging trends and relevant previous research. In this chapter we will deal with the theories about credit risk management at the telecommunications company level in order to solve the research problem. This chapter will focus on: theoretical review, types of risk, management of loan portfolio & credit reporting, credit reference review and lastly chapter summary firstly define credit risk management and evaluate its effectiveness and then consider the general principles of credit risk management at the telecommunications company level. The current economic environment has intense competitive pressures, rising default rates, increasing levels of consumer and commercial debt and volatile economic conditions, an organisations ability to effectively manage and monitor its credit risk could mean the difference between success and survival (Altman, 2012). In the past decade companies that were performing well, have seen dramatic profit losses due to poor credit exposures, interest rate positions taken, or derivative exposures that may or may not have been assumed to hedge balance sheet risk (Santomero, 1997). In a universal response to the crisis telecommunication companies have embarked on a necessary upgrade of their risk management and control systems. Although prudent credit management, sufficient bad and doubtful debt provisions can cushion the telecommunications company from risk, they are still exposed to default risk from borrowers due to the nature of their business. In order to improve its growth and expansion, be stable and maintain its stability a telecommunications company must operate profitability. These companies have faced various challenges that have threathend their profitiablity ‘ In the last twenty years, the telecommunications companies sector has faced various challenges that include non-performing loans (NPL), political interference and fluctuations of interest rate among others, which have threatened the telecommunications companies stability. According to Shubhasis (2005), risk management is important to bank management because telecommunications companies are “risk machines” they take risks; they transform them and embed them in Josiah Aduda, Ph.D., lecturer and chair, Department of Accounting & Finance, School of Business, University of Nairobi. James Gitonga, MBA student, Department of Accounting & Finance, School of Business, University of Nairobi. RELATIONSHIP BETWEEN CREDIT RISK MANAGEMENT AND PROFITABILITY telecommunications companies products and services. Risks are uncertainties resulting in adverse variations of profitability or in losses. Various risks faced by commercial institutions include credit risk, market risks, interest rates risk, liquidity risk, and operational risk. Various local studies conducted have failed to establish any relationship between credit risk management and profitability in commercial telecommunications companies in Kenya. The study by Mudiri (2003) had sought to determine credit management techniques applied by financial institutions in Kenya. Maina (2003) conducted a survey on risk based capital standards and the riskiness of bank portfolio in Kenya. Mwirigi (2006) conducted an assessment of credit risk techniques in commercial telecommunications companies. The study by Ngare (2008) was a survey of credit risk management practices by commercial telecommunications companies in Kenya. While the above research outcome provides valuable insights on credit risk management, they have not induced a clear relationship between credit risk management and profitability in Commercial telecommunications companies in Kenya. Given the gaps poised by the above empirical studies, this study poses the research question: “what is the relationship between credit risk management and profitability in commercial telecommunications companies in Kenya?” The study hypothesizes that commercial telecommunications companies should demonstrate to improve profitability of the bank after administration of a credit. This hypothesis is based on the argument that commercial telecommunications companies’ largest credit risk is loans, although that credit risk exists throughout the other activities of the bank both on and off the balance sheet. These other activities include acceptance, inter-bank transactions, trade financing, foreign exchange transactions, futures, swaps, options and guarantees. To answer the above question, the study seeks to establish a relationship between credit risk management and profitability; this will be done by reviewing various profitability measures and in particular the ROE (return on equity) ratio. ROE has an important indicator to measure the profitability of the telecommunications companies has been discussed extensively. Guo (2005) indicated that the efficiency of telecommunications companies can be measured by using the ROE which illustrates to what extent telecommunications companies use reinvested income to generate future profits. The main objective of this paper is to establish a relationship between credit risk management and profitability of commercial telecommunications companies in Kenya. . Literature Review Credit is derived from a Latin word “credere” meaning trust. When a seller transfers his wealth to a buyer who has agreed to pay later, there is a clear implication of trust that payment will be made at agreed date. Major causes of serious telecommunications companies problems are directly related to lax credit standards for borrowers. Poor portfolio assessment or lack of attention to changes in economic circumstances, common in emerging economies (Lopez, 1999). Telecommunications companies as financial institutions extend credit to their customers in form of loans, overdrafts, off balance sheet activities (i.e., letter of credit (LC) guarantees), and credit card facilities. Telecommunications companies grant credit to enhance their revenues streams, maintain a competitive edge, to act as its bargaining power in the industry, as the industry practice as well as to enhance the relationship with their customers. The fundamental objective of the bank management is to maximize shareholders wealth. This goal is interpreted to mean maximizing the market value of the firm’s ordinary shares. Wealth maximization, in turn, requires that managers evaluate the present value of cash flows under uncertainty with larger, near-term cash flows proffered when evaluated on a risk adjusted basis. To obtain higher yields on returns, a bank must either take an increased risk or lower operating costs. Thus managers must evaluate and balance the tradeoffs between the opportunity for higher returns, the probability of not realizing those returns, and the possibility that the bank might fail (Koch & MacDonald, 2006). RELATIONSHIP BETWEEN CREDIT RISK MANAGEMENT AND PROFITABILITY 936 A credit policy helps to define the frame work within which credit will be extended and managed. Hempel, Simonson, and Coleman (1994) stated that there are two credit evaluation systems in relation to telecommunications companies assessment of loan applications. Judgmental credit analysis which relies on the consumer loan officer’s experience in assessing the loan and empirical credit analysis also referred to as credit scoring which assesses applicants based on scores applied to various applicant characteristics. Examples of applicant characterizes assessed include age, employment history, performance on loans currently held and types of accounts held (Shubhasis, 2005). Profitability is the primary goal of all business ventures. Without profitability the business will not survive in the long-run. So measuring current and past profitability is very important. Profitability is measured by income and expenses. Income is generated from the activities of the business. A business that is highly profitable has the ability to reward its owners with a large return on the investment (Waweru & Kalani, 2009). A profitable telecommunications companies sector is better able to withstand negative shocks and contribute to the stability of the financial system. Important changes in the operating environment particularly credit risk is likely to affect bank profitability. Empirical analysis finds that both bank-specific as well as macroeconomic factors are important determinants in the profitability of telecommunications companies (Ross, Westerfield, Jordan, & Jaffe, 2007). Credit Risk According to Gregoriou and Hoppe (2008), bank loan is a debt, which entails the redistribution of the financial assets between the lender and the borrower. The bank loan is commonly referred to the borrower who got an amount of money from the lender, and need to pay back, known as the principal. In addition, the telecommunications companies normally charge a fee from the borrower, which is the interest on the debt. The risk associated with loans is credit risk. One of the principal duties of financial institutions is to provide loans, this is typically the source of income to telecommunications companies, bank loans and credit also constitute one of the ways of increasing money supply in the economy (Waymond, 2007). The Central Bank of Kenya (CBK) defines NPLs as those loans that are not being serviced as per loan contracts and expose the financial institutions to potential losses. It is important to note that non-performing loans refer to accounts whose principal or interest remains unpaid 90 days or more after due date. According to the Central Bank of Kenya Supervision Report, the level of non-performing loans has been increasing steadily from shs.56 billion in 1997, to shs.83 billion in 1998 to shs.97 billion in 1999. This high level of non-performing loans continues to be an issue of major supervisory concern in Kenya. The recent financial crises in USA and Europe suggest that NPL amount is an indicator of increasing threat of insolvency and failure. However, the financial markets with high NPLs have to diversify their risk and create portfolios with NPLs along with performing loans, which are widely traded in the financial markets. In this regard, Germany was one of the leaders of NPL markets in 2006 because of its sheer size and highly competitive market (Misati, Njoroge, Kamau, & Ouma, 2010). According to Misati et al. (2010), as pressure mounts on the telecommunications companies industry’s profitability resulting from over reliance on interest income by telecommunications companies, it is strategically imperative that telecommunications companies focus on other revenue streams. National Industrial Credit Bank [NIC] has introduced new products to diversify revenue and to keep its head above the water. They added that part of NIC Bank’s strategy has been to diversify revenues, by expanding the scope of its activities in addition to its predominant asset finance focus and offering more general commercial telecommunications companies facilities and other products. Premium financing and provision of custodial RELATIONSHIP BETWEEN CREDIT RISK MANAGEMENT AND PROFITABILITY services have reduced over reliance on interest income. Several telecommunications companies in the country are already harvesting the fruits of prudent risk management practices. According to the Basel Accords, telecommunications companies face various risks, that are, credit risk, market risk and operational risk. Market risk is defined as the risk of losses in on and off-balance sheet positions arising from movements in market prices. The capital treatment for market risk addresses the interest rate risk and equity risk pertaining to financial instruments, and the foreign exchange risk in the trading and telecommunications companies books. Operational risk is defined as the risk of direct or indirect loss resulting from inadequate or failed internal processes, people and systems or from external events. A firm wide risk management framework is an amalgam of strategy, process, infrastructure and environment which helps such institutions make intelligent risk taking decisions prior to committing limited resources and then helps to monitor the outcome of these decisions (Altman & Sabato, 2005). This integration approach to managing risks ensures full risks identification, risk awareness, risk assessment, measurement and control and finally evaluation. In response to recent corporate and financial disasters, regulators have increased their examination and enforcement standards. In telecommunications companies sector, Basel II has established a direct linkage between minimum regulatory capital and underlying credit risk, market risk and corporate risk exposure of telecommunications companies. This step gives an indication that capital management is an important stage in risk mitigation and management. However, development of effective key risk indicators and their management pose significant challenge. Some readily available sources such as policies and regulations can provide useful direction in deriving key risk indicators and compliance with the regulatory requirement can be expressed as risk management indicators. A more comprehensive capital management framework enables a bank to improve profitability by making better risk based product pricing and resource allocation (Altman & Sabato, 2005). Because of the dire consequences of credit risk, it is important that credit managers perform a comprehensive evaluation of credit risk covering the credit portfolio management, lending function and operations, credit risk management policies, non performing loans portfolio, asset classification and loan provisioning policy. This review must be done at least annually. Credit risk management is the process of evaluating risk in an investment. When the risk has been identified, investment decisions can be made and the risk vis a vis return balance considered from a better position. Credit risk can be reduced by monitoring the behavior of clients who intend to apply for credit in the business. An important aspect in credit risk management is credit assessment. Due to the dire effects of credit risk, whereby if not well managed can lead to bank failure, it is important for a bank to have capacity to assess, administer, supervise, control, enforce and recover loans, advances, guarantees and other credit instruments (Joetta, 2007). It is the responsibility of management to set up a credit administration team to ensure that once credit is granted it is properly maintained and administered. Procedures for measuring a firms overall exposure to credit risk as well as stringent internal rating systems should be adequate. All companies that do not currently have independent risk management structures must immediately set up units that will concentrate fully on the risk management function. This risk management function within an institution should report directly to the board, to ensure independence. The importance of credit risk management has never been more important with the current high default rates and high provisioning. Indeed in 1999, at the end of the benign credit cycle, telecommunications companies, regulators and financial market practitioners were spending considerable time on this subject due to increased emphasis on sophisticated risk management techniques in a challenging environment, refinements in credit RELATIONSHIP BETWEEN CREDIT RISK MANAGEMENT AND PROFITABILITY scoring techniques, establishment of relatively large database of defaults, recoveries and credit mitigations, development of offensive credit risk mitigation techniques such as securitizations, credit derivatives and credit insurance products (Altman, 2002). Financial institutions use various techniques of mitigating credit risk. The most common are collateral, guarantees, netting off of loans against deposits of the same counter-party. The payments are netted off against the receipts and the balance is paid thus reducing the credit risk. Credit Insurance, factoring, debt collection, surety bonds, and letter of credit are other techniques widely used. While use of these techniques will reduce or transfer credit risk, other risks may arise which include legal, operational, liquidity and market risks (Stutz, 1985). The dictum in finance says that “The greater the risk, the higher the return”. Therefore risk can be seen both as an opportunity and as a threat; opportunity, because the most risky businesses are also highly profitable. Risk is a threat because it includes a possibility of losing part or the whole of your investment. Risk cannot however be done away with. Venkat (1999) argued that most business managers would agree that it is neither possible nor desirable to completely eliminate risk from the business proposition. What is required is an understanding of all risks that arises from a particular business and managing those risks effectively. The purpose of Basel II is to create an international standard about how much capital telecommunications companies need to put aside to guard against the types of risk telecommunications companies face. In practice, Basel II tries to achieve this by setting up meticulous risk and capital management requirements aimed at ensuring that a bank holds capital reserves appropriate to the risks the bank exposes itself to. These rules imply that the greater risk which bank is exposed to, the greater the amount of capital a bank needs to hold to safeguard its solvency (Montgomery, 2005). The theoretical telecommunications companies literature is, however, divided on the effects of capital requirements on bank behavior and consequently, on the risks faced by the institutions. Some academic works point toward that capital requirement clearly contributes to various possible measures of bank stability. On the contrary, other works conclude that capital requirements make telecommunications companies riskier institutions than they would be in the absence of such requirements (Joetta, 2007). Vanhouse (2007) has discovered numerous aspects that explain the differing implications of portfolio-management models for the responsiveness of bank portfolio risk to capital regulation. Results depend on telecommunications companies being either value-maximizing or utility-maximizing firms; bank ownership (if limited liability) and whether telecommunications companies operate in complete or incomplete asset markets. Moreover, the effects of capital regulation on portfolio decisions and therefore on the telecommunications companies system’s safety and soundness eventually depend on which perspective dominates among insurers, shareholders, and managers in the principal-agent interactions CREDIT RISK MANAGEMENT Credit Risk Management Greuning and Bratanovic (2003) defined credit risk as the chance that a debtor or a financial instrument issuer will not be able to pay interest or repay the principal according to the terms specified in a credit agreement. It means that payments may be delayed or ultimately not paid at all, which may cause cash flow problems and affects telecommunications companies liquidity. Credit risk is the most important area in risk management. More than 80% of all telecommunications companies balance sheet relate to credit. All over the world exposure to credit risk has led to many telecommunications companies failure. Credit risk exposure particularly to real estate led to widespread telecommunications companies problems in Switzerland, Spain, The United Kingdom, Sweden, Japan and others. Here in Kenya, Obiero (2002) found that credit risk was only second to poor management in contributing to bank failures. On perception, Idarus (2005) found that credit risk was the most important area of risk management in Kenya. Risk management means, increasing the likelihood of success, reducing the possibility of failure and RELATIONSHIP BETWEEN CREDIT RISK MANAGEMENT AND PROFITABILITY limiting the uncertainty of all the overall financial performance. Best (2001) argued that the purpose of risk management is to prevent an institution from suffering unacceptable loss. He went on to explain that “unacceptable loss” is one which either causes an institution to fail or materially damages its corporate position. Telecommunications companies must monitor the ever changing micro and macroeconomic environment to identify the risks therein and find ways of managing these risks. Developing economies in the world, Kenya included, face more uncertainties that the developed counter parts. Telecommunications companies business in developing worlds therefore faces more risks. Failure to manage risks effectively in the respective telecommunications companies leads to bank failures. One bank failure may have a contagion effect on the other telecommunications companies leading to a systematic failure of the whole telecommunications companies industry in a country or even a whole region as witnessed during the Asian Bank crisis (1997-1998). Kenya has had its share of bank failures. Obiero (2002) noted that in 1993 alone, 14 telecommunications companies in Kenya collapsed. In recognition of the high risks involved in telecommunications companies, the Central Bank of Kenya published risk management guidelines for the purpose of providing guidance to all financial institutions on the minimum requirements for a risk management frame work and strategy. It has classified the risks facing financial institutions into nine classes namely: strategic risk, credit risk, liquidity risk, interest rate risk, price risk, foreign exchange rate risk, operational risk, reputation risk and regulatory risk. Telecommunications companies can project the average level of credit losses it can reasonably expect to experience. Bank Bank Performance Measures Brealey and Myers (2003) argued that there are various important measures in determining profitability of an organization. These include: net profit margin, return on assets, and return on equity. In 1972, David Cole introduced a procedure for evaluating bank performance via ratio analysis. This procedure enables an analyst to evaluate the source and magnitude of telecommunications companies profits relative to selected risks taken. Performance measures derive directly from the income statement. There are various measures of profitability. The ratio of net income to equity is the accounting return on equity (ROE). It often serves as a target profitability measure at the overall bank level. Market Return on Equity, is a price return, or the ratio of the price variation between two dates of the telecommunications companies shares. Under some specific conditions, for example when the price earnings ratio remains constant, it can serve as a profitability benchmark. Both ROE and the market return on equity should be in line with shareholders expectations for a given level of risk of the telecommunications companies shares. Return on assets (ROA) is another measure of profitability for telecommunications companies transactions. The most common calculation of ROA is the ratio of the current periodical income, interest income and current fees, dividend by asset balance. ROA can be decomposed into four constituent’s parts by an accounting identity: Profitability = ROA = NI/TA + NII/TA – OV/TA – LLP/TA (1) where, NI is net interest income, NII is non-interest income, OV is non-interest overhead expenses and LLP is loan loss provisioning (Ross et al., 2007). The net interest margin NI/TA creates a wedge between returns to savers and investors and reflects the cost of bank intermediation services and the efficiency of the telecommunications companies sector. In general, the higher the net interest margin, the higher are telecommunications companies’ profit margins and more stable is the telecommunications companies sector. However, a higher net interest margin could reflect riskier lending practices associated with substantial loan loss provisions and could be an indication of inefficiency in the telecommunications companies sector (Ross et al., 2007). The drawback of accounting ROE and ROA measures, and of the P&L (profit and loss) of the trading portfolio, is that they do not include any risk adjustment. Hence, they are not comparable from one borrower to another, because their credit risk differs, RELATIONSHIP BETWEEN CREDIT RISK MANAGEMENT AND PROFITABILITY from one trading transaction to another, and because the market risk varies across products. This drawback is the origin of the concept of risk-adjusted performance measures. This is an incentive for moving, at least in internal reports of risks and performances to economic values, mark to market or mark to model values, because these are both risk and revenue adjusted. The hypothesis tested was as follows: H1: There is a significant relationship between credit risk management and bank performance. Data and Methodology Research Design, Sample Selection, Data Sources and Description of Variables The paper used descriptive research design. This is deemed appropriate because the study involved an in depth study of the credit risk management and profitability in commercial telecommunications companies in Kenya which helped the researcher in describing the state of current affairs. According to O. M. Mugenda and A. G. Mugenda (1999), a descriptive study is undertaken in order to certain and be able to describe the characteristics of the variables of interest in a situation. Other studies by Kombo (1997) and Situma (2006) successfully employed descriptive analytical technique. The research comprised of all the commercial telecommunications companies in Kenya as at 31st December 2010, licensed and registered under the Telecommunications companies Act. According to O. M. Mugenda and A. G. Mugenda (1999), a target population is one the researcher wants to generalize the result of the study. According to the central Bank of Kenya, there were 44 licensed telecommunications companies as at 31st December 2009. A random sample of 30 financial institutions was taken from the population. This constitutes 73% of the entire population. This sample fairly represented the whole population and was considered large enough to provide a general view of the entire population and serve as a good basis for valid and reliable conclusion. The study applied data from both primary and secondary sources. Primary data was collected by use of a questionnaire. Kothari (2009) argued that questionnaires generate data in a systematic and ordered fashion. The questionnaire comprised both of structured and unstructured questions to avoid being too rigid and to quantify the data especially where structured items were used. The questionnaire was administered through the “drop and pick later” method. The follow-up was done by emails, Short Message Service (SMS) and phone calls, on arrangements some questionnaires were personally administered to the respondents. The researcher also used secondary sources. The data for the telecommunications companies was extracted from the telecommunications companies’ annual reports and financial statements for a ten year period 2000-2009. These were obtained from the NSE library, the respective telecommunications companies’ secretaries, and the telecommunications companies supervision department at the central bank of Kenya. The researcher used ROE as the indicator of the profitability in the regression analysis, because ROE has been widely used in earlier research. In addition, use of ROE as the indicator of profitability enhanced accuracy in that the required information was available in the annual reports of the telecommunications companies. The researcher chose NPLR (NPL ratio) as the independent variable because it is an indicator of risk management which affects profitability of telecommunications companies. NPLR indicates how telecommunications companies manage their credit risk because it defines the proportion of NPL amount in relation to TL amount. Other researchers who have used NPLR to measure credit risk include Ara, Bakaeva, and Sun (2009), and Brewer and Jackson (2006). NPLR is defined as NPLs divided by TLs (total loans). To calculate this ratio, the researcher used data provided in the annual reports of each bank for a period of ten years (i.e., from 2000 to 2009). NPL amount is provided in the notes to financial statements under loans section. TL amount, the denominator of the ratio, has been gathered by adding two types of loans: loans to institutions RELATIONSHIP BETWEEN CREDIT RISK MANAGEMENT AND PROFITABILITY 941 and loans to the public. The loan amount is provided in the balance sheet of the telecommunications companies in their annual reports. Thus, calculation of the NPLR has been accomplished in following way: NPLR = (NPL amount) ÷ (TL amount) The Empirical Model The regression analysis was conducted to find out the relationship between credit risk management and profitability in commercial telecommunications companies: The researcher employed the following regression model presented below: Y = α + β1X (3) That is: (1) Standard: Y—The value of dependent variable; α—The constant term; β—The coefficient of the function; X—The value of independent variables. (2) Application: Y—ROE-profitability indicator; NPLR—credit risk management indicator. Thus the regression equation becomes: ROE = α + βNPLR (4) It is the regression function which determines the relation of X (NPLR) to Y (ROE). α is the constant term β is the coefficient of the function, it is value for the regression equation to predict the variances in dependent variable from the independent variables. This means that if β coefficient is negative, the predictor or independent variable affects dependent variable negatively: one unit increase in independent variable will decrease the dependent variable by the coefficient amount. In the same way, if the β coefficient is positive, the dependent variable increases by the coefficient amount. α is the constant value which dependent variable predicted to have when independent variables equal to zero (if X = 0 then α = Y). Regression analysis output contains the following variables: R2 is the proportion of variance in the dependent variable that can be predicted from independent variables. There is also adjusted R2 which gives more accurate value by avoiding overestimation effect of adding more variables to the function. So, high R2 value indicates that prediction power of dependent variable by independent variables is also high. Adjusted R2 is calculated using the formula 1-((1-R2) × ((N-1)/(N-k17-1)) × 18. The formula shows that if the number of observations is small the difference between R2 and adjusted R2 is greater than 1, since the denominator is much smaller than numerator. Adjusted R2 sometimes gives negative value. Since R2 is adjusted to find out how much fit probably happen just by luck: the difference is amount of fit by chance. Also, negative values of adjusted R2 occur if the model contains conditions that do not help to predict the response (ROE) or the predictor (NPLR) chosen are wrong to predict ROE. R2 is generally considered to be secondary importance, unless the primary concern is of using regression equation to make accurate predictions. R2 is an overall measurement of the strength of association, and does not reflect how any independent variable is associated with the dependent variable. The probability value (P-value) is used to measure how reliably the independent variables can predict the dependent variable. It is compared to the significance level which is typically 0.05. If the P-value is greater than 0.05, it can be said that the independent variable does not show a statistically significant relationship with the dependent variable. RELATIONSHIP BETWEEN CREDIT RISK MANAGEMENT AND PROFITABILITY Credit analysis In its most simplistic form, credit can be defined as “nothing but the expectation of the repayment of a sum of money within a limited time” and credit risk is “the probability that this expectation will not be met” (Caouette, Altman, Narayanan & Nimmo 2008). Therefore, credit risk management forms a critical part of the procedures that the telecommunications company will use to avoid losing money and managing its bad debt rate. Credit risk management is presented in a simple way in the book, Managing Credit Risk, by authors Caouette, Altman, Narayanan and Nimmo According to them, there is a similarity between a telecommunications company dealing with credit risk and a tailor designing a suit for a customer – “carefully measuring customer’s need and capacities to make sure the financing is a good fit” (Caouette, Altman, Narayanan, Nimmo 2008, xv). More specifically it is a process of analysis where the objective is to “look at both borrower and the credit extension being proposed and to assign a risk rating” (Caouette, Altman, Narayanan, Nimmo 2008, 106). The question is, whether to approve a loan for a specific customer or not, is the most important question that the act of credit analysis helps the company to answer. The company makes profit by extending the correct cell phone facility to the customer, and its goal is to do this at the lowest risk possible. The relevant members of the credit team will assess the details of the customer’s behaviour as well as any supporting documentation rendered, the staff member will utilise several methods to verify and qualify the information presented during the loan assessment and lending procedure. Finally they will take the necessary actions that decide whether to approve or reject the application for a cell phone contract. Amongst the list of important tasks and questions that the telecommunications company’s use in credit analysis, one of these is the credit bureau information pertaining to the customer being assessed, the credit bureau system is a very powerful tool that the organisation may use to analyse the data of a particular customer and make a prediction of the value of the credit extension he/she will be given. In the case of telecommunications the credit extended would normally include a handset or device and a voice and/or data package, that will accompany the device and this is normally repayable over a period of two years. The telecommunications company has an internal credit rating system which is a complex system used for facility approval, portfolio monitoring and management reporting. These systems analyse the adequacy of the loan-loss reserves or capital, the profitability and the loan-pricing analysis (William & Mark 2013, 168). With these systems, the credit risk is identified and measured with the most accuracy as it applies the company’s policies and strategies to minimise the credit risk that may be present. When applying for the cell phone facility, debtors need to provide information such as their identity information, basic financial data, and demographic information etc. The system will add to this information to a credit bureau record that it will electronically source from the credit bureau of its choice. By fetching information from the credit bureau relevant to the customer and it will then automatically make calculations based on the compounded information and the result will be called the credit rate. Credit policies and strategies The credit risk and the bad debt rate is controlled by a strict set of credit policies and strategies which include, risk measurement, , risk identification risk control techniques, risk grading techniques and other legal documents. (Bangladesh BANK Telecommunications company report 2005). Also in this report, Bangladesh Telecommunications company pointed out that the credit policies and strategies are very important to the telecommunications company’s operation and revenue targets. Good credit policies need to have the following criteria: - Provide detail credit evaluation - Provide risk identification, measurement, monitoring and control - Define the target market, risk acceptance criteria, credit approval authority and guideline to portfolio management. (Bangladesh Telecommunications company report, 2005) Greuning and Bratanovic (2009) divided credit risk into three main categories based on their aims,the policies in the first group aim to limit or reduce the credit risk, the second one aims to classify the assets while the third set deals with loss provision (Greuning & Bratanovic 2009, Large telecommunication companies may have many types of written policy however, the most important policy are the lending guidelines. Beside the credit policies, credit risk strategies are also important for any telecommunications company, its main purpose being to determine the risk appetite of the company. “Credit risk strategies are designed to optimise return while credit risk is kept within the predetermined limit.”(Bangladesh Telecommunications company report 2005.) Like the credit policies, credit strategies are key measurements for assessment and in the guidelines of Bangladesh Telecommunications company, there are the following fundamental points: - Credit strategies should include a statement of the telecommunications company’s willingness to grant facilities based on the type of economic activity, currency, geographical location, market etc. -Target market, business sectors, level of diversification and concentration, cost of bad debt and cost of granting credit are also key points that receive focus - Credit strategies should delineate the company’s overall risk that relates to credit risk and the granting credit plans is based on this in-depth analysis - Credit strategies should inform the pricing strategies.(Bangladesh Telecommunications company 2005) Lending guidelines As part of the credit portfolio of the company a set of lending guidelines are established which is the set of official instructions to be used in the company’s main activity, which is extending credit for a cell phone facility. The credit extension guidelines, in general, are an important set of tools that will help the telecommunications company’s staff members when dealing with applications. A mortgage lending policy, for example, will be a list that would specify how the mortgage loans are organised, assessed, approved, supervised and collected. The lending policies also include the following fundamental points: - Lending authority - Type of loans and distribution by category - Appraisal process - Credit pricing and maturities - Collection monitoring - Limit on total outstanding credit extension. (Greuning & Bratanovic 2009, 166–170) At the telecommunications company level, credit analysis, credit policies, credit strategies and lending guidelines are the four core methods that are used in the credit risk management process. Based on these four core methods, the telecommunications company may have many practical ways to control the bad debt and credit risk. KENYA Theoretical Review Various theories on credit risk management have been brought to the fore by numerous scholars and these include; adverse selection theory, credit rationing theory, information rationing theory and moral hazard theory. Adverse Selection Theory The adverse selection theory paper originated by Stiglitz and Weiss (1981) rests on two main assumptions: that loan contracts are subject to limited exposure (i.e. if project returns are less than debt obligations, the borrower bears no responsibility to pay out of pocket), and that lenders cannot distinguish between borrowers of different degrees of risk. The analysis will be restricted to involuntary default, i.e. it will assume that borrowers will repay their loans within their means to do so In the world of credit extension where simple debt contracts exist between risk-neutral borrowers and lenders, there is a presence of limited liability of borrowers and this imparts a preference for risk among borrowers, and a corresponding aversion to risk among lenders. The stance of limited liability of the borrowers results in lenders bearing all the downside risk, and conversely all returns above the loan repayment obligation will accrue to borrowers. As the interest rates rise it directly affects the profitability of the lower risk borrowers disproportionately, thereby causing them to drop out of the loan application pool. This the results in an adverse compositional effect—where higher interest rates increases the riskiness of the average applicant pool. Subsequently at very high Interest rates, the applicant pool is limited to those who could potentially generate very high return, but presumably with lower risk probability. It is then probable that excess demand in the credit market may persist even in the face of competition and flexible interest rates. In the adverse selection theory, the interest rate may not raise enough to guarantee that all loan applicants secure credit, in times when loanable funds are limited. In general, the volume of credit and level of effort is less than the first-best. Borrowers who have greater wealth to put as collateral obtain cheaper credit, have incentives to work harder, and earn more income as a result. Existing asset inequalities within the borrowing class are projected and possibly magnified into the future by operation of the credit market, a phenomenon that may cause the persistence of poverty. By exchange information about their customers telecommunications companies can improve their knowledge of applicants' characteristics and behavior. In Principles, this reduction of informational asymmetries can reduce adverse selection problems in the lending, as well as change borrowers' incentives to repay, both directly and by changing the competitiveness of the credit market. Pagano and Jappelli (1993) show that information sharing reduces adverse selection by in improving bank's information on credit applicants. In their model, each bank has private information about local credit applicants, but has no information about non-local applicants. If telecommunications companies exchange information about their client's credit worthiness, they can assess also the quality of non-local credit seekers, and lend to them as safely as they do with clients. Information sharing can also create incentives for borrowers to perform in line with telecommunications companies' interest. Klein (1982) shows that information sharing can motivate borrowers to repay loans, when the legal environment makes it difficult for telecommunications companies to enforce credit contacts. In his model borrowers repay their loans because they know that defaulters will be blacklisted, reducing external finance in future Credit Rationing Theory This theory was introduced by Freimer and Gordon (1965) and comprehensively by Stiglitz and Weiss (1981). According to the seminal Stiglitz and Weiss (1981) paper, unsatisfied agents are borrowers. Asymmetric information leads to credit rationing, as lenders cannot distinguish between high quality and low quality borrowers. However, this dominate view is not without criticism. In particular, De Meza and Webb (1987) vigorously contest this result. They show that asymmetric information in credit markets can lead to the inverse result, which is an excess of credit (over lending). Telecommunications companies exist because they screen and monitor borrowers more efficiently than other investors can (Allen and Santomero, 1998). They are specialized in gathering private information and treating it (Freixas and Rochet, 1999). Managing money and deposit accounts, telecommunications companies own highly strategic information on firms’ receipts and expenditures as well as the way that firms develop (Diamond and Rajan, 2001). Despite this plethora of information, relationships between bankers and firms are not perfect. Telecommunications companies suffer from informational asymmetries (Freixas and Rochet, 1999) such that evolution of prices (interest rates) cannot clear the credit market. Finally, non-walrassian equilibrium arises with a fringe of unsatisfied agents. The more interesting form of credit rationing is equilibrium rationing, where the market had fully adjusted to all publicly, i.e. why telecommunications companies ration credit free, available information and where demand for loans for a certain market interest rate is greater than supply. Stiglitz and Weiss (1981) proved that credit rationing occurs if telecommunications companies charge the same interest rate to all borrowers, because they cannot distinguish between borrowers and screening borrowers perfectly is too expensive. Both assumptions are very simplifying and do not occur in this manner in the real world. Telecommunications companies are usually able to distinguish their borrowers up to a certain degree. Moreover, telecommunications companies face more than only two types of borrowers. Telecommunications companies usually charge more than just one interest rate to all customers. High risk borrowers pay a higher interest rate and credit rationing is less likely. However, telecommunications companies cannot distinguish borrowers perfectly and screening them perfectly is impossible. Thus, credit rationing may occur. According to Stiglitz and Weiss (1981) adverse selection and thus credit rationing still occurs if telecommunications companies require collateral. They argue that low-risk borrowers expect a lower rate of return on average. Thus, they are less wealthy than high-risk borrowers on average after some periods. Low-risk borrowers are therefore not able to provide more collateral. Increasing collateral requirements may have the same adverse selection effect as a higher interest rate. Instead Bester (1985) argues that telecommunications companies only offer contracts in which they simultaneously adjust interest rates and collateral requirements. He proved that there is always a combination of interest rate and collateral requirements so that credit rationing does not occur. In adverse selection models, there is typically too little trade (i.e., there is a so-called "downward distortion" of the trade level compared to a "first-best" benchmark situation with complete information), except when the agent is of the best possible type (which is known as the "no distortion at the top" property). The principal offers a menu of contracts to the agent; the menu is called "incentive-compatible" if the agent picks the contract that was designed for his or her type. In order to make the agent reveal the true type, the principal has to leave an information rent to the agent (i.e., the agent earns more than his or her reservation utility, which is what the agent would get if no contract was written). Adverse selection theory has been pioneered by Roger Myerson, Eric Maskin, and others in the 1980s.[8][9] More recently, adverse selection theory has been tested in laboratory experiments and in the field.[10][11] Information Sharing Theory Research on information sharing is relatively recent and growing. Earlier papers analyze the effect of information sharing in a market with asymmetric information, either moral hazard or adverse selection (Gehrig and Stenbacka, 2005). In moral hazard setups, information sharing may provide borrowers with higher incentives to perform: because information becomes available to competitor telecommunications companies, borrowers are happy to perform better because they no longer fear being held- up by the lender-monopolist (Padilla and Pagano (1997). Second, borrowers do not want to (strategically) default, because this will be publicly known: when default in- formation is shared, borrowers will face an increase interest rates and a decrease in access to finance not only by the current bank, but by the rest of telecommunications companies in the market - the so called disciplinary effect (Padilla and Pagano, 2000). More-over, information sharing resolves adverse selection problems when telecommunications companies have ex ante informational advantage, as in Pagano and Jappelli (1993), and Padilla and Pagano (2000). By sharing information, telecommunications companies may learn about those good and bad borrowers of the competitor telecommunications companies who (exogenously) switched from the previous telecommunications companies. Gehrig and Stenbacka (2001), however, identify a dark side of information sharing. Rather than starting with ex-ante informational advantage, their adverse selection model considers a two-period competition with symmetric knowledge in period one. In their location model, when telecommunications companies have less incentives to acquire information for too many customers in period one, when they know they will have to compete away rents on them by sharing information in period two. They show that if information about borrowers' true becomes known to other telecommunications companies, second-period competition will be higher and first-period interest rates will have to go up. As a result, information sharing can lead to welfare losses. However, they assume that all characteristics about true types can be revealed to the outside bank. In contrast, we distinguish between information that can be shared (hard) and information that cannot (soft), relationship specific information. Hauswald and Marquez (2003) show that information processing, providing the screening bank with more informational advantage, will safeguard it from competition allowing to earn rents. Advances in the screening technology, therefore, will increase returns from screening. Access to that same information, on the other hand, levels the playing field for telecommunications companies and erodes their rents due to increased competition. Thus, technological progress that allows for easier access to the incumbent's information will decrease the returns to investing in such information. Moral Hazard Theory Moral hazard refers to the risk that a party to a transaction has not entered into the contract in good faith, has provided misleading information about its assets, liabilities or credit capacity, or has an incentive to take unusual risks in a desperate attempt to earn a profit before the contract settles. Problems of moral hazard in telecommunications companies and other financial institutions were evident at many stages of the recent financial crisis (Myerson, 2011). As Freixas and Rochet (1997) have noted, modern microeconomic models of telecommunications companies depend on advances in information economics which was not available when the traditional Keynesian and monetarist theories were first developed. So now, as economists confront the need for deeper insights into the forces that can drive macroeconomic instability, we should consider new models that can apply the microeconomic theory of telecommunications companies to the macroeconomic theory of business cycles. In modern macroeconomic theory economic growth rate depends, crucially, on the efficiency of financial institutions. The financial systems themselves depend on accurate information about borrowers and the project the funds are used for (Chakraborty and Play, 2001) Types of Risks In the literature of credit rating, there are various types of risks. These include; portfolio at risk, credit risk management and liquidity risk management. 2.3.1 Portfolio at Risk (PAR) The loan portfolio at risk is defined as the value of the outstanding principal of all loans in arrears, expressed as a percentage of the total loan portfolio currently outstanding. Portfolio at Risk (PAR) is a standard international measure of portfolio quality that measures the portion of a portfolio which is deemed at risk because payments are overdue. For example; PAR 30 means the portion of the portfolio whose payments are more than 30 days past due. PAR 30 above 5 or 10% is a sign of trouble in microfinance. High delinquency makes financial sustainable impossible for an institution. Portfolio at risk rates measure the outstanding balance of loans that are not being paid on time against the outstanding balance of total loans (Brown, 2006). McIntosh and Wydick (2004), conclude that credit information systems first create a screening effect that improves risk assessment of loan applicants, thereby raising portfolio quality, which in turn reduces rates of arrears. The international standard for measuring bank loan delinquency is portfolio at risk (PAR). Both the numerator and the denominator of the ratio are outstanding balances. The numerator is the unpaid balance of loans with late payments, while the denominator is the unpaid balance on all loans. The PAR uses the same kind of denominator as an arrears rate, but its numerator captures all the amounts that are placed at increased risk by the delinquency. A PAR can be pegged to any degree of lateness. PAR, a common measure among telecommunications companies, captures the outstanding balance of all loans with a payment more than 90 days late. Credit Risk Management Credit risk is the current or prospective risk to earnings and capital arising from an obligor’s failure to meet the terms of any contract with the bank or if an obligor otherwise fails to perform as agreed. The largest source of credit risk is loans. However, credit risk exists throughout the other activities of the bank both on and off the balance Sheet. An effective and sound credit risk management is critical to the stability of an institution. Institutions use various techniques of mitigating credit risk. The most common are collateral, guarantees and netting off of loans against deposits of the same counter-party. While the use of these techniques will reduce or transfer credit risk, other risks may arise which include legal, operational, liquidity and market risks. Therefore there is a need for a bank to have stringent procedures and processes to control these risks and have them well documented in the policies. At present, in this jurisdiction, the common credit risk mitigation technique used is collateral. One of the factors that telecommunications companies consider when deciding on a loan application is the estimated chances of recovery (CBK, 2010). To arrive at this, information is needed on how well the applicant has paid past loans. This information is vital because there is usually a definite relationship between past and future performance in loan repayment. Liquidity Risk Management Liquidity Risk is the current or prospective risk to earnings and capital arising from a bank’s inability to meet its liabilities when they fall due without incurring unacceptable losses. It arises when the cushion provided by the liquid assets are not sufficient to meet its obligations (CBK, 2010). The prerequisites of an effective liquidity risk management include an efficient systems and procedures. An effective measurement and monitoring system is essential for adequate management of liquidity risk. Liquidity risk is the risk that the Bank will encounter difficulty in meeting obligations from its financial liabilities. The Bank’s approach to managing liquidity is to ensure, as far as possible, that it will always have sufficient liquidity to meet its liabilities when due, under both normal and stressed conditions, without incurring unacceptable losses or risking damage to the Bank’s reputation. The Bank’s treasury maintains a portfolio of short-term liquid assets, largely made up of short-term liquid investment securities, loans and advances to telecommunications companies and other inter-bank facilities, to ensure that sufficient liquidity is maintained within the Bank as a whole. The daily liquidity position is monitored and regular liquidity stress testing is conducted under a variety of scenarios covering both normal and more severe market conditions. The key measure used by the Bank for managing liquidity risk is the ratio of net liquid assets to deposits from customers. Management of Loan Portfolio and Credit Reporting Credit administration is critical in ensuring the soundness of the credit portfolio. It is the responsibility of management to set up a credit administration team to ensure that once a credit is granted it is properly maintained and administered. Credit reporting is a critical Part of the financial system in most developed economies but is often weak or absent in developing countries. It addresses a fundamental problem of credit markets: asymmetric information between borrowers and lenders that can lead to adverse selection and moral hazard. MANAGING CREDIT RISK: THE CHALLENGE FOR THE NEW MILLENNIUM Dr. Edward I. Altman Stern School of Business New York Universityhttp://pages.stern.nyu.edu/~ealtman/2-%20CopManagingCreditRisk.pdf The heart of a credit report is the record it provides of an individual's or a firm's payment history, which enables lenders to evaluate credit risk more accurately and lower loan processing time and costs. Credit reports also strengthen borrower discipline, since non payment with one institution results in sanctions with others. Credit reporting, it shows, significantly contributes to predicting default risk of potential borrowers, which promotes increased lending activity (Miller, 2003). Credit Reference Bureaus Review Empirical review constitutes a review of literature from various documents such as published or unpublished reports, dissertation papers, and journals, Sessional papers, and academic handbooks among others. The purpose of this research is to evaluate the effectiveness of credit reference bureaus in commercial telecommunications companies in Kenya. Finding e mpirical evidence of CRBs’ is inherently difficult and very little is factually known about their operations. Overview of Development of Credit Reference Bureaus According to World Bank (2009) survey, data collected reveals that almost 60 countries have Public credit registries (PCRs). PCRs contain information on the performance of borrowers in a financial system and are administered and maintained by either the central bank or bank Supervisor. The region with the highest coverage of public credit registries is Latin America, where 17 countries have established PCRs, including all the largest economies (Argentina, Brazil, Chile, Colombia, and Mexico). The first countries to establish public credit registries were in Western Europe – Germany in 1934 followed by France in 1946. By the mid-1960s, three other European countries – Italy, Spain and Belgium – had also established PCRs. Early adopters included the former French colonies in Western Africa which formed the West African Monetary Union in 1962 and immediately established public credit reporting following the French example. Also several Middle Eastern and North African nations adopted PCRs in the 1950s and 1960s (Egypt, 1957; Tunisia, 1958; Morocco, 1966; Jordan, 1966; and Turkey, 1951. The PCRs in Argentina and Brazil were established in the 1990s in response to financial crises also with the primary goal of supporting telecommunications companies supervision. Over time, though, these registries were transformed to also enhance the information to private financial institutions. Credit Reference Bureaus in the Developing World Throughout the developing world, the growing availability of consumer credit and the growing competition between Financial institutions have made the necessity of credit information sharing all the more apparent. However, the extent and efficiency of information sharing mechanisms vary greatly between countries and continents. Africa remains the region of the world with the least developed credit information systems, yet the exploding financial sectors in many African countries have sparked interest in the feasibility of the creation of credit bureaus to help manage borrower risk under heightened competition. Latin America arguably has the most extensive coverage of credit information systems among developing regions, with credit information sharing recently being extended even into the microfinance sector. A pertinent example is Bolivia. Prior to 1999 Bolivian law forbade the existence of private credit bureaus (Campion, 2001), believing credit data was too sensitive and important a topic to entrust to the private sector. 2.5.3 Credit Reference Bureaus in Kenya The operations, establishment, licensing, governance and management of CRBs, is provided through the telecommunications companies (Credit Reference Bureau) regulations, 2008. Establishment and licensing of credit reference bureaus in Kenya, is through an entity incorporated as a limited company under the companies Act and application for a license is made through the central bank of Kenya. A bureau licensed may engage activities such as: store and update the customer information maintain database and generate reports and assess the creditworthiness of a customer. In addition; may carry out market and statistical research and sell to institutions specialized literature. The Central Bank of Kenya has been mandated by law to license and supervise the operations of such bureaus; many borrowers make a lot of effort to repay their loans, but do not get rewarded for it because this good repayment history is not available to the bank that they approach for new loans. On the other hand, whenever borrowers fail to repay their loans telecommunications companies are forced to pass on the cost of defaults to other customers through increased interest rates and other fees. Put simply - good borrowers are paying for bad. This is coming to an end with the adoption Credit Reference Bureau. It involves credit reports; in this case a report is generated by the Credit Reference Bureau, containing detailed information on a person's credit history, including information on their identity, credit accounts and loans, bankruptcies and late payments, and recent enquiries. It can be obtained by prospective lenders only when they have permissible reason as defined in law, to determine his or her creditworthiness. Credit reporting allows telecommunications companies to better distinguish between good and bad borrowers. Someone who has failed to pay their loan at one bank will not simply be able to walk to another bank to get another loan without the telecommunications companies knowing about it. Over time better information on potential borrowers should mean that it will be both cheaper and easier to obtain loans. These credit reports provide a credit score that is unique to a customer’s character. This credit score is a measure of credit risk calculated from a credit report using a standardized formula. A positive score is characterized by frequently paid bills; lack of defaults on outstanding balances; maintaining steady employment; On the other hand, a negative credit score is characterized by late payments; bankruptcy; fraud charges; liens or foreclosures; loss of employment. It is worth noting that sharing of negative credit information does not amount to blacklisting. However, such information is expected to be taken into account by telecommunications companies while assessing applications for loans and other bank facilities. 2.6 Non-Performing Loans A non-performing loan (NPL) may be defined as a loan that has not been receiving payments for ninety days or more. The magnitude of non-performing loans is a key element in the initiation and progression of financial and telecommunications companies crises (Tiffany and Greenidge, 2010). Grosvenor et al (2010) observed that, the current global financial crisis, which began in the United States, is attributed to the August 2007 collapse of the sub-prime mortgage market and that commercial telecommunications companies with greater risk appetite and that are more willing to make loans with a higher probability of default, tend to record higher losses Further, that the level of NPLs in the US started to increase substantially in early 2006 in all sectors. NPLs are therefore a measure of the stability of the telecommunications companies system, and thereby the financial stability of a country. From the above, it is clear to see and appreciate why the ability to forecast, monitor and manage non-performing loans is important. NPLs reflect credit risk for telecommunications companies arising either from external factors such as depressed economic conditions, or internal factors such as poor lending decisions or both. The ratio of NPLs to assets is an indicator of a bank’s asset quality and financial soundness. In the case of the current financial turmoil, a high ratio may indicate that telecommunications companies are not healthy since they have significant exposure to the origins of the problem. According to (Ng’etich, 2001), controlling NPAs is very important for both the performance of an individual bank and the economy’s financial environment. Due to the nature of their business, commercial telecommunications companies expose themselves to the risks of default from borrowers. Prudent credit risk assessment and creation of adequate provisions for bad and doubtful debts can cushion the telecommunications companies risk. 2.7 Empirical Review The Empirical Model The regression analysis was conducted to find out the relationship between credit risk management and profitability in commercial telecommunications companies: The researcher employed the following regression model presented below: Y = α + β1X (3) That is: (1) Standard: Y—The value of dependent variable; α—The constant term; β—The coefficient of the function; X—The value of independent variables. (2) Application: Y—ROE-profitability indicator; NPLR—credit risk management indicator. Thus the regression equation becomes: ROE = α + βNPLR (4) It is the regression function which determines the relation of X (NPLR) to Y (ROE). α is the constant term β is the coefficient of the function, it is value for the regression equation to predict the variances in dependent variable from the independent variables. This means that if β coefficient is negative, the predictor or independent variable affects dependent variable negatively: one unit increase in independent variable will decrease the dependent variable by the coefficient amount. In the same way, if the β coefficient is positive, the dependent variable increases by the coefficient amount. α is the constant value which dependent variable predicted to have when independent variables equal to zero (if X = 0 then α = Y). Regression analysis output contains the following variables: R2 is the proportion of variance in the dependent variable that can be predicted from independent variables. There is also adjusted R2 which gives more accurate value by avoiding overestimation effect of adding more variables to the function. So, high R2 value indicates that prediction power of dependent variable by independent variables is also high. Adjusted R2 is calculated using the formula 1-((1-R2) × ((N-1)/(N-k17-1)) × 18. The formula shows that if the number of observations is small the difference between R2 and adjusted R2 is greater than 1, since the denominator is much smaller than numerator. Adjusted R2 sometimes gives negative value. Since R2 is adjusted to find out how much fit probably happen just by luck: the difference is amount of fit by chance. Also, negative values of adjusted R2 occur if the model contains conditions that do not help to predict the response (ROE) or the predictor (NPLR) chosen are wrong to predict ROE. R2 is generally considered to be secondary importance, unless the primary concern is of using regression equation to make accurate predictions. R2 is an overall measurement of the strength of association, and does not reflect how any independent variable is associated with the dependent variable. The probability value (P-value) is used to measure how reliably the independent variables can predict the dependent variable. It is compared to the significance level which is typically 0.05. If the P-value is greater than 0.05, it can be said that the independent variable does not show a statistically significant relationship with the dependent variable. RELATIONSHIP BETWEEN CREDIT RISK MANAGEMENT AND PROFITABILITY 942 Results and Discussions Descriptive Analysis As per the response rate analysis, out of the 30 questionnaires that were issued to the respondents, 28 which translate to 93% were returned, while 2 which translate to 7% were not returned. The 7% consist of respondents who were on leave and could not be contacted during the time of questionnaire collection. 93% represent a high response rate. The questionnaires were given to credit managers and finance managers in respective commercial telecommunications companies. Gender analysis indicates that 61% of the respondents were male, while 39% were female. This shows that most of the respondents who participated in the study were male. An analysis of the highest education levels attained by the respondents shows that 21% had acquired diploma, 43% bachelor’s degree while 36% had masters. This shows that majority had at least bachelors degree thus had an understanding of their work and related issues. Regarding work experience 22% of the respondents had worked with their institutions for less than five years. 39% had worked for 5-10 years, 32% for 10-15 years and 7% for 15 years and above. Majority therefore had been in their telecommunications companies institutions for 5-10 years which is a period enough to understand the individual firms and the telecommunications companies industry. The first step in analyzing the data was through descriptive measures and scatter graph, which was done using Microsoft Excel. The results were as shown in Table 1 (descriptive measures) and Figure 1 (scatter plot) below. Table 1 Descriptive Statistics Mean Std. deviation Return on equity 12.9333 21.94800 NPLR 0.1845 0.16028 Note. Source: research data. The average value of ROE had a mean of 12.93 and a standard deviation of 21.95 while NPLR had a mean of 0.1845 with a standard deviation of 0.161. There is very high variability in both ROE and NPLR among the commercial telecommunications companies during the period 2000 to 2009 as shown by their standard deviation values. y = -72.035x + 26.236 R2 = 0.2772 -200 -150 -100 -50 0 50 100 0 0.2 0.4 0.6 0.8 1 NPLR ROE Figure 1. Scatter plot. Source: research data. RELATIONSHIP BETWEEN CREDIT RISK MANAGEMENT AND PROFITABILITY 943 The scatter plot shows a downward trend for the relationship between ROE and NPLR. The trend seems to be linear and as such a linear regression analysis can be used for further analysis. Empirical Results On whether there is a relationship between credit management and profitability 75% of the respondents felt that there was a relationship between credit management and profitability while 25% disagreed. This indicates that there is a relationship between credit management and profitability. When asked about the application of the credit management principles in the telecommunications companies institutions, majority of the respondents indicated that credit management principles were widely used in the telecommunications companies and even microfinance institutions. In determining the effect of credit management on profitability, 68% of the respondents indicated that credit management affects profitability while 32% felt that credit management had no effect on profitability. This shows that credit management affects profitability. On whether credit management principles were applicable in the telecommunications companies institutions, 93% of the respondents indicated that credit management principles were applicable in their telecommunications companies institutions while 7% denied. This shows that credit management principles are applicable in the various telecommunications companies institutions. They gave examples of credit management principles used as creating value, explicitly addressing uncertainty, basing on the best available information and taking into account human factors. They also cited on the six Cs of credit management which include character, capability, context, credibility, collateral and conditions. Various ratios are used to ascertain profitability. 36% of the respondents indicated that their telecommunications companies institutions used return on income ratio. 46% indicated that they used Return on equity ratio while 18% indicated that they used cash return on asset ratio. From this, it can be concluded that return on equity is the commonly used ratio followed by return on income. When asked on the procedure of determining profitability most of the respondents pointed out return on equity which is the amount of net income returned as a percentage of shareholders equity. Return on equity measures a corporation’s profitability by revealing how much profit a company generates with the money shareholders have invested. Return on Equity is expressed as a percentage and calculated as net income divided by shareholder’s equity. Regarding the effect of credit ratios on credit management, 61% of the respondents indicated that profit ratios affect credit management while 39% disagreed. This shows that credit ratios affect credit management. The extent to ratios relate to credit risk management. 25% of the respondents indicated that it was very great extent, 39% great extent and 36% average extent. This shows that profitability ratios greatly affect credit risk management. In models estimation, this paper adopts the generalized least square (GLS) method of estimation instead of the ordinary least square (OLS) to estimate a linear regression formed. Table 2 Pearson Correlation Return on equity NPLR Return on equity 1.000 NPLR -0.527 1.000 Note. Source: research data. Correlation matrix is used to check on the concept of multicollinearity, that is if the is a strong correlation RELATIONSHIP BETWEEN CREDIT RISK MANAGEMENT AND PROFITABILITY 944 between two predictor variables. In this case there was only one predictor variable hence the problem of multicollinearity did not exist. There also exist a moderate negative correlation between ROE and NPLR (see Table 2). Table 3 Model Summary R R square Adjusted R square Std. error of the estimate Change statistics R square change F change df1 df2 Sig. F change 0.527(a) 0.278 0.275 18.68179 0.278 114.690 1 298 0.000 Note. Predictors: (Constant), NPLR. Source: research data. Analysis in Table 3 shows that the coefficient of determination (the percentage variation in the dependent variable being explained by the changes in the independent variables) R2 equals 0.278, that is, NPLR explain 27.8% of ROE for commercial telecommunications companies leaving 72.2% unexplained. The P-value of 0.000 (less than 0.05) implies that the model of ROE is significant at the 5% level of significance. Table 4 ANOVA Sum of squares Df Mean square F Sig. Regression 40,027.940 1 40,027.940 114.690 0.000 (a) Residual 104,004.726 298 349.009 Total 144,032.667 299 Notes. Predictors: constant, NPLR. Dependent variable: return on equity. Source: research data. ANOVA findings (P-value of 0.00) in Table 4 show that there is correlation between the predictor’s variables (NPLR) and response variable (ROE). The study used regression analysis to find the association between NPLR and ROE forecasting model was developed and tested for accuracy in obtaining predictions. The finding of the study indicated that the model was moderately significant. This is demonstrated in the part of the analysis where R2 for the association between NPLR and ROE was 27.8%. The results obtained from the regression model show that there is an effect of credit risk management on profitability on reasonable level with 27.8% possibility of NPLR in predicting the variance in ROE. So, the credit risk management strategy defines profitability level to an important extent for commercial telecommunications companies in Kenya. Table 5 Coefficients of Regression Equation Unstandardized coefficients Standardized coefficients T Sig. B Std. error Beta Constant 26.251 1.646 15.947 0.000 NPLR -72.189 6.741 -0.527 -10.709 0.000 Notes. Dependent variable: return on equity. Source: research data. As shown in Table 5, the established simple linear regression equation becomes: Y = 26.25 – 72.19X (5) RELATIONSHIP BETWEEN CREDIT RISK MANAGEMENT AND PROFITABILITY 945 where: Constant = 26.25, shows that at zero value of NPLR for all commercial telecommunications companies, ROE takes the value 26.25. X1= -72.19, shows that one unit change in NPLR results in 72.19 units decrease in ROE. NPLR is also linearly related with ROE as shown, its P-value of 0.00 which is less than 0.05. Many studies have illustrated how comprehensive information helps lenders better predict borrower default. Kallberg and Udell (2003) found that historical information collected by a credit bureau had powerful default predictive power. A study by Barron and Staten (2003) showed that lenders could significantly reduce their default rate by including more comprehensive borrower information in their default prediction models. An analogous study – specific to Brazil and Argentina – found similar default rate decreases when more information was available on borrowers (Powell, et al. 2004).Credit markets present asymmetric information problems. Lenders know neither the past behavior and the characteristics, nor the intentions of credit applicants. This creates a moral hazard problem that causes lenders to make credit decisions based on the average characteristics of borrowers rather than on individual characteristics (Rothschild and Stiglitz, 1976). Moral hazard implies a lower average probability of payment, making credit more expensive. Stiglitz and Wise (1981) states that higher interest rates exacerbate informational problem, adverse selection, because only higher risk borrowers are willing to accept loans at high interest rates . Additionally, those borrowers that have defaulted with a particular lender are the ones looking for alternative credit sources (Akerlof, 1970). This increases the average risk of lending and the corresponding interest rate. Credit is hence allocated to excessively risky projects, and low risk borrowers face tighter credit constraints. Adequately managing credit risk in financial institutions (FIs) is critical for the survival and growth of the FIs. In the case of telecommunications companies, the issue of credit risk is of even of greater concern because of the higher levels of perceived risks resulting from some of the characteristics of clients and business conditions that they find themselves in. In recent decades, a large number of countries have experienced financial distress of varying degrees of severity, and some have suffered repeated bouts of distress (Hardy, +1998). Pazarbasioglu (1999) believes that the best warning signs of financial crises are proxies for the vulnerability of the telecommunications companies and corporate sector. He showed that full blown telecommunications companies crises are associated more with external developments, and domestic variables are the main leading indicators of severe but contained telecommunications companies distress. He adds that the most obvious indicators that can be used to predict telecommunications companies crises are those that relate directly to the soundness of the telecommunications companies system. 35 In the 1980's and early 1990's, several countries in developed, developing and transition economies experienced several telecommunications companies crises requiring a major overhaul of their telecommunications companies systems. Kenya has experienced telecommunications companies problems since 1986 culminating in major bank failures (37 failed telecommunications companies as at 1998) following the crises of; 1986 - 1989, 1993/1994 and 1998 (Kithinji and Waweru, 2007; Ngugi, 2001). Presently, several developed countries including the USA are experiencing a telecommunications companies crisis. For example the Citibank group alone, has written off more than $39 billion in losses (Elliot, 2008). The Kenyan telecommunications companies sector was in the 80’s and 90’s saddled with a momentous Non Performing Loans (NPLs) portfolio. This invariably led to the collapse of some telecommunications companies. One of the catalysts in this scenario was ―Serial defaulters‖, who borrowed from various telecommunications companies with no intention of repaying the loans. Undoubtedly these defaulters thrived in the ―information asymmetry‖ environment that prevailed due to lack of a credit information sharing mechanism. The development of a sustainable information sharing industry is therefore recognized as a key component of financial sector reforms in almost all developing and emerging economies (CBK, 2010). 36 Herausgeber (2001) observed that the use of credit risk information systems has become a topic of analysis and promotion within international organizations and national governments. He states that one of the factors limiting the access to credit for micro enterprises is the lack of information on the risk that they represent to the financial intermediaries. As a result, the commercial telecommunications companies need to make a bigger effort to complete the information they require in order to make decisions over the credit requests they receive, incrementing their operational costs, which are generally transferred to their customers directly or indirectly. Credit service users are generally classified in five categories according to their financial record and capacity. The categories range from A to E or from 1 to 5, depending on the country, indicating increased levels of risk. Users classified as A or 1 are customers who have a minimal or non-existent risk level, to which a premium rate is offered; while those who are classified as E or 5 present the highest risk. Bank supervisory authorities demand that the regulated institutions set aside reserves according to the customer’s risk level, which reaches 100% of the loan in the highest risk category. This implies a financial cost, which is transferred to the customers through the interest rate and other charges. The telecommunications companies sector is generally classified in the C and D categories (in other words, high-risk customers). This risk is compensated with rates over 37 the premium interest rate, which makes their access to financing very expensive. Given that the telecommunications companies sector is an important component in the national economies, the formal and informal financial intermediaries are demanding information about their real and potential customers in order to better evaluate the risk level they present. On the other hand, the central bank and government are supporting the use of institutional information services as a way of reducing costs of lending and reducing financial risks and barriers to entry for other credit suppliers, all of which should translate into an increase in the credit supply and other financial services for the economy. These efforts are supported by evidence that indicates that where credit bureaus are operating, most of the telecommunications companies consult the credit risk databases in order to decide whether to grant consumer credit and even more so to grant micro enterprise credit. Additionally, the information obtained from the registries has been better valued than other sources of information used in evaluating credit worthiness, even more than guarantees and financial statements (CBK, 2010). Telecommunications companies play a central role in extending financial services within an economy. In support of this role, credit bureaus help lenders make faster and more accurate credit decisions. Credit histories not only provide necessary input for credit underwriting, but also allow borrowers to take their credit history from one financial institution to another, thereby making lending markets more competitive and, in the end, more affordable. Credit Reference Bureaus (CRBs) assist in making credit accessible to more people, and enabling lenders and businesses reduce risk and fraud. Andrew Powell et al (2004), states that Information problems have long been at the fore of analyses of credit markets. Indeed, one rationale for telecommunications companies as institutions is to gather information and establish relationships with borrowers in an effort to surmount these problems. A striking feature of telecommunications companies is the amount of services that they offer and the economies of scope between them. For example, accounts and payments’ services provide valuable data to the bank on the creditworthiness of clients as potential borrower. Jappelli and Pagano (1993), in a model with adverse selection, show that exchanging information on borrower type decreases default rates and reduces average interest rates. In a related paper, Padilla and Pagano (1997) show that information sharing among borrowers would lead to lower interest rates and increased lending. Locally, various aspects of CRB have been reviewed by various scholars. Sigei (2010) researched on evaluating the effectiveness of credit reference bureau in Kenya. The case of KCB. His study revealed that CRBs play an important role in preventing serial loan 39 Chapter2: Literature Review This chapter presents the theories and articles relevant to the thesis topic of “Critical success factors for effective risk management procedures in financial industries”. It is divided into the following two parts: (1) Risk management and (2) Critical success factors for risk management. This chapter helps the reader understand the basics of risk management and emphasizes our critical success factors for effective risk management procedures. 2.1 Risk management 2.1.1 Risk management overview Over the last few decades, risk management has become an area of development in financial institutions. The area of financial services has been a business sector related to conditions of uncertainty. The financial sector is the most volatile in the current financial crisis. Activities within the financial sector are exposed to a large number of risks. For this reason, risk management is more important in the financial sector than in any other sectors (Carey, 2001). Carey regards financial institutions as the main point of risk-taking in an uncertain environment. a) What is risk? Risk is a function of the likelihood of something happening and the degree of losing which arises from a situation or activity. Losses can be direct or indirect. For example, an earthquake can cause the direct loss of buildings. Indirect losses include lost reputation, lost customer confidence, and increased operational costs during recovery. The chance of something happening will impact the achievement of objectives (Partnerships BC, 2005 and NIST, 2004). “Risks are usually defined by the adverse impact on profitability of several distinct sources of uncertainty. While the types and degree of risks an organization may be exposed to depend upon a number of factors such as its size, complexity business activities, volume etc” (SBP, 2003, p.1) Risk can be classified into systematic and unsystematic risk (Al-Tamimi and Al-Mazrooei, 2007). Systematic risk refers to a risk inherent to the entire system or entire market. It is sometimes called market risk, systemic risk or un-diversification risk that cannot be avoided through diversification. Whereas, unsystematic risk is risk associated with individual assets and hence can be avoided through diversification. It is also known as specific risk, residual risk or diversifiable risk. 6 b) What is the risk management? Risk management can be defined in many ways. For example, Anderson and Terp (2006) maintain that basically, risk management can be defined as a process that should seek to eliminate, reduce and control risks, enhance benefits, and avoid detriments from speculative exposures. The objective of risk management is to maximize the potential of success and minimize the probability of future losses. Risk that becomes problematic can negatively affect cost, time, quality and system performance. The Committee of Sponsoring Organizations of the Treadway Commission (Committee of Sponsoring Organizations, 2004, p.2) defines risk management as follows: “Enterprise risk management is a process, effected by an entity’s board of directors, management and other personnel, applied in strategy setting and across the enterprise, designed to identify potential events that may affect the entity, and manage risk to be within its risk appetite, to provide reasonable assurance regarding the achievement of entity objectives” Risk management is the process to manage the potential risks by identifying, analyzing and addressing them. The process can help to reduce the negative impact and emerging opportunities. The outcome may help to mitigate the likelihood of risk occurring and the negative impact when it happens (Partnerships BC, 2005). Risk management involves identifying, measuring, monitoring and controlling risks. The process is to ensure that the individual clearly understands risk management and fulfills the business strategy and objectives (SBP, 2003). Based on the definition above, the meaning of risk involves: • The likelihood and consequence of something occurring. • The chance of something happening impacting the achievement of objectives. And risk management is about: • The process to eliminate, reduce and control risks. • It involves identifying, analyzing, measuring, monitoring and controlling risks • Reducing the negative and emerging opportunities. • Achievement of business strategy and objectives. In order to facilitate a better understanding of risk management, the authors will describe the important process of risk management. Ergo, the following review will explain the publication of risk management frameworks. 7 2.1.2 The risk management procedures Longman dictionary and BusinessDictionary.com gave the definition of “procedure” as. • Longman dictionary explained the definition of “procedure” was “a way of doing something, especially the correct or usual way (process).” • And BusinessDictionary.com gave the definition of “procedure” was “fixed, step-by-step sequence of activities or course of action (with definite start and end points) that must be followed in the same order to correctly perform a task. Repetitive procedures are called routines. See also method.” The procedures of risk management have recently been published in a few papers. It was found in a previous publication that the risk management process is described slightly differently by different authors. According to SBP (2003), a risk management framework encompasses the scope, the process/system/procedures to manage risks and the roles and responsibilities of the individual related to risk management. The effective risk management framework includes the risk management policies and procedures that cover risk identification, acceptance, measurement, monitoring, reporting and control. The National Institute of Standards and Technology (NIST, 2004) reviews the risk management procedures in three parts: risk assessment, risk mitigation and evaluation and assessment. The risk assessment process includes identification, evaluation of risk impact and recommendation of risk-reducing measures. Secondly, risk mitigation involves prioritizing, maintaining and implementing the appropriate risk-reducing controls recommended by the risk assessment. Lastly, evaluation and assessment emphasize the continual evaluation process and the key factors for a successful risk management program. The Enterprise-wide Risk Management Guideline describes the model and the process to manage risk according to the following eight sequence steps: (1) Establishing the context (2) Identifying (3) Analyzing (4) Evaluating (5) Developing the risk mitigation strategy (6) Monitoring and Reviewing the risk mitigation strategy (7) Quantifying the risks and (8) Consulting and communicating the risk (Partnerships BC, 2005). Standards Australia and Standards New Zealand (2004) and the International Organization for Standardization (ISO/DIS 31000, 2008) design the model of risk management procedures in the same way. The process is comprised of five activities to establish the context of risk, risk assessment which is composed identifying risks, analyzing risks and evaluating risks, risk treatment, communication and consultation, and monitoring and controlling risk events. So the framework for the risk management process presented by Standards Australia and Standards New Zealand (2004) will be the model for this study. The risk management process consists of seven iterative sub-processes shown in figure 1, which follows. Figure 1: Details of the risk management process (source: Standards Australia and Standards New Zealand (2004)) http://umu.diva-portal.org/smash/get/diva2:233985/FULLTEXT01.pdf Communicate and consult Communication and consultation aim to identify who should be involved in the assessment of risk including identification, analysis and evaluation and who will be involved in the treatment, monitoring and reviewing of risk. Those people should understand the basis of decision-making and the reason why particular actions are required (Standards Australia and Standards New Zealand, 2004). 2. Establish the context By establishing the context, the organization defines the parameters to be taken into account when managing risk, and sets the scope and risk criteria for the remaining process. This process needs to be considered in greater detail and particularly how it relates to the scope of the particular risk management process. Standards Australia and Standards New Zealand (2004) provides a five-step process to assist with establishing the context within which risk should be identified: • The external context – is the external environment in which the organization seeks to achieve its objectives. • The internal context – the internal environment in which the organization seeks to achieve its objectives. • The risk management context – defines the objectives, strategies, scope and parameters of the activities of the organization or those parts of the organization where the risk management process is being applied or should be established. • Develop risk evaluation criteria – the organization should develop criteria that should be used to evaluate the significance of risk and define acceptable levels of risk for a specific activity or event and decide what is unacceptable. • Define the structure of risk analysis – isolate the categories of risk which are managed. The structure will provide greater depth and accuracy in identifying significant risks. 3. Risk identification Risk identification is the basic step of risk management. This step reveals and determines the potential risks which are highly occurring and other events which occur very frequently. Risk is investigated by looking at the activity of organizations in all directions and attempting to introduce the new exposure which will arise in the future from changing the internal and external environment. Correct risk identification ensures risk management effectiveness (Tcankova, 2002). 10 4. Risk analysis Risk analysis is concerned with assessing the potential impact of exposure and likelihood of the particular outcome actually occurring. The impact of exposure should be considered under the elements of time, quality, benefit and resource. This step determines the probability and consequences of a negative impact and then estimates the level of risk by combining the probability and consequences (Standards Australia and Standards New Zealand, 2004). 5. Risk evaluation Before determining the probability, it is essential to consider risk tolerance. The organizations will consider “risk appetite” (the amount of risk they are willing to take) and decide upon acceptable or unacceptable risk. The acceptable level of risk depends upon the degree of voluntaries. Risk evaluation is important for making sense in specific situations and provides adequate material for decision making (Vrijling, Hengel and Houben, 1995). This step is about deciding whether risks are acceptable or need treatment. 6. Risk treatment Risk treatment involves selecting and implementing one or more options for treating risks. Standards Australia and Standards New Zealand (2004) offer the following options for risk treatment: avoid risk, change the likelihood of occurrence, change the consequences, share risk and retain risk (residual risk may be retained if it is at an acceptable level). 7. Monitoring and review Monitoring and review is an essential and integral step in the risk management process. Risk needs to be monitored to ensure the changing environment does not alter risk priorities and to ensure the risk management process is effective both in design and in operation. The organization should review at least on an annual basis (Standards Australia and Standards New Zealand, 2004). The process of risk management illustrates cyclical nature of the process. It should be an integral of management. The next step will describe the critical success factors influence to risk management procedures. 11 2.2 Critical Success Factors for effective risk management 2.2.1 Critical Success Factors overview As the starting point, the definition of Critical Success Factors (CSFs) are introduced by Rochart (1979, p.84). He defines Critical Success Factors as “The limited number of areas in which results, if they are satisfactory, will ensure successful competitive performance for the organization. They are the few key areas where things must go right for the business to flourish. If results in these areas are not adequate, the organization’s efforts for the periods will be less than desired”. Boynton and Zmud (1984) discuss CSF methodology, define CSFs and review a range of uses of the CSF method in the first part of their article. They regard Critical Success Factors as one of the few things that ensures success for an organization. Critical success factors are maintaining a high performance for an organization’s currently operating activities and its future. Moreover, Freund (1988) explained the CSFs concept as the most important for overall organizational objectives, mission and strategies. Critical Success Factors which are appropriate to each unit of business and overall organization aim to fulfill the organization’s objectives. A great number of factors are extremely difficult to focus on and therefore only five to ten should be indicated. The following review of Critical Success Factors will discuss Critical Success Factors for effective risk management. There are a number of papers on Critical Success Factors contributing to risk management. Grabowski and Roberts (1999) examine the problem of risk mitigation and suggest a process designed to support the high level of performance in an organization. They identify the four important factors as: 1. Organizational Structuring and Design 2. Communication 3. Organizational Culture 4. Trust Galorath (2006) focuses on the importance of risk management, the essence of risk management and assesses the processes to implement risk management. He argues that risk management requires five activities, which are as follows: 1. Top-level management support 2. An integral part of the entire program management structure and processes 3. The participation of everyone involved 4. Cultural imperative 5. A pattern of measurement 12 Carey (2001) reviews the Turnbull’s approach for risk management. He describes the Turnbull report and how to apply this approach in order to manage risk. The approach can be summarized in the nine main issues which are: 1. The importance of sound judgment 2. Identification issues 3. Keeping control of your reputation 4. Assessing the importance of risks 5. Verifying your judgments 6. Changing management 7. Embedding risks 8. Cultural challenges 9. Remuneration issues Hasanali’s paper (2002) is related to management in an organization. This study maintains that the success of knowledge management depends upon many factors. In the point of view of the authors, there are some interesting factors which should be adopted to risk management. We need to identify and examine these factors for our study. Hasanali’s critical success factors can be categorized into five categories: 1. Leadership 2. Culture 3. Structure, roles, responsibilities 4. Information technology infrastructure 5. Measurement NSW Department of State and Regional Development (2005) proposes a practical guide for managing risk which provides a basic understanding of risk management in small businesses. This document helps to implement the risk management process. In the last part of this guide, it is argued that a business needs to adopt risk management because effective risk management is important. Therefore, risk management should include: 1. Ensuring appropriate commitment to risk management 2. Setting clear objectives and guidelines for risk management 3. Allocating adequate resources 4. Training staff appropriately 5. Implementing systems for monitoring and reviewing risks Stiglitz and Weiss Òscar Jordà U.C. Davis 2 Idea: show that in a competitive equilibrium a loan market may be characterized by credit rationing. Mechanism: the interest rate a bank charges may itself affect the riskiness of a pool of loans by either: 1. sorting potential borrowers – adverse selection 2. affecting the actions of borrowers – moral hazard Information asymmetry: borrowers have different probability of repayment but telecommunications companies can’t identify “good” borrowers from “bad.” Hence prices act as a screening device. Stiglitz and Weiss Òscar Jordà U.C. Davis 3 Incentives: higher prices cause borrowers to select riskier projects. Expected returns r *ˆ r bank optimal rate Stiglitz and Weiss Òscar Jordà U.C. Davis 4 The price mechanism may not clear the loan market if interest rates go above * ˆ r since the bank would be attracting worse risk. Hence the bank’s best strategy is to ration credit whenever demand pushes interest rates above * ˆ r. Credit Rationing – Definitions: 1. given loan applicants that appear equal, some receive a loan and some do not even when they offer to pay a higher interest rate. 2. some individuals unable to get a loan under one supply schedule at any interest rate would get a loan under a larger schedule. Stiglitz and Weiss Òscar Jordà U.C. Davis 5 Interest Rates as screening devices ƒ Let θ index projects ƒ Let R be gross returns, distributed F(R, θ) ƒ Mean preserving spread: if two projects have the same expected return but θ1 > θ2, then θ1 is considered a riskier project ƒ Let B be the amount borrowed; and C the collateral. Then default occurs when ) ˆ 1( r B R C + ≤ + ƒ Let the net return to the borrower be ) ˆ, ( r R π , hence ) ; )ˆ 1( max( )ˆ, ( C B r R r R − + − = π ƒ The return to the bank is ))ˆ 1( ; min( )ˆ, ( r B C R r R + + = ρ Stiglitz and Weiss Òscar Jordà U.C. Davis 6 Adverse Selection: ƒ Theorem 1: Let θ ˆ denote the value of θ s.t. expected returns are 0, i.e. [] 0 )ˆ , ( ; )ˆ 1( max 0 = − + − ∫ ∞ θ R dF C B r R, then if θθ ˆ >, the firm borrows. ƒ Theorem 2: 0 ˆ ˆ > rd d θ , that is, as r ˆ increases, the pool of applicants becomes riskier. This can be shown by differentiating the previous expression. ƒ Theorem 3: 0 < θ ρ d d, bank returns are a decreasing function on the riskiness of the loan. Stiglitz and Weiss Òscar Jordà U.C. Davis 7 ƒ Theorem 4: Expected returns are non-monotonic r1r2 ρ both types apply only risky types apply Stiglitz and Weiss Òscar Jordà U.C. Davis 8 ƒ Theorem 5: existence of credit rationing depends on ρ (r) being nonmonotonic. Moral Hazard ƒ Theorem 7: If, at a given interest rate r, a risk-neutral firm is indifferent between two projects, an increase in the interest rate results in the firm preferring the project with the high probability of bankruptcy (but higher expected return). Stiglitz and Weiss Òscar Jordà U.C. Davis 9 Example Project A’s (B’s) return in the good state, Ra , (Rb); 0 in the bad state, with a bR R > Let ) ( b a p p denote the probability of project A (B) succeeding, then b a p p > Assume: (1) b b a a R p R p > Expected returns: () ()C p B r R p E C p B r R p E b b b B a a a A ) 1( ) 1( ) ( ) 1( ) 1( ) ( − − + − = − − + − = π π Stiglitz and Weiss Òscar Jordà U.C. Davis 10 Then ) ( ) (B AE E ππ > iff C B r p p R p R p b a b b a a − + > − − ) 1( Let * r be such that ) ( ) (B AE E ππ = so that the previous expression holds with equality. Then, ƒ if * r r < then ) ( ) (A BE E ππ < (i.e. the firm chooses the low risk project. ƒ if * r r > then ) ( ) (A BE E ππ > (i.e. the firm chooses the high risk project. Stiglitz and Weiss Òscar Jordà U.C. Davis 11 Returns to the lender: ƒ if * r r < then C p B r pa a) 1( ) 1(− + + ƒ if * r r > then C p B r pb b) 1( ) 1(− + + Note: C p B r pa a) 1( ) 1(− + + > C p B r pb b) 1( ) 1(− + + Hence the lender will choose * r r