Chapter 3: Credit Scoring Techniques Instructor: 1 Adapted by Dr. Ray Saadaoui Mallek CHAPTER THREE Credit Scoring Techniques 2 LEARNING OBJECTIVES List the development of credit scoring techniques Discuss the behavioural aspects of credit scoring Explain the imperative for credit scoring Discuss the application of credit scoring techniques List the various modelling techniques used for credit scoring Discuss the steps to take in implementing a credit scoring process 3 INTRODUCTION The use of statistical credit scoring techniques allows for rigorous and disciplined decision-making The concept of informal credit scoring has a long history Computer advances in 1980s saw far greater application of formal credit scoring techniques 4 OVERVIEW Credit scoring generally used as a statistical method of ranking the probability of loan repayment/default with three basic characteristics: Must not rely on prohibited information (e.g. race, religion, gender) Must contribute positively to a client’s creditworthiness Credit extended should contribute positively to the lending institution 5 OVERVIEW Ultimate aim of credit scoring is to improve the credit quality of the lending institution’s loan book Credit scoring is increasingly moving from consumer lending to small business and even to corporate lending activities. 6 THE DEVELOPMENT OF STATISTICAL CREDIT SCORING The Development of Statistical Credit Scoring Statistical developments in the 1930s–1940s allowed identification of good/bad loans Significant growth in post-WWII (post-World War II period) consumer credit such as credit cards Provided a non-emotive, rigorous and statistically valid method for determining credit decisions Computing technology in 1980s allowed development of sophisticated credit scoring methods 7 THE DEVELOPMENT OF STATISTICAL CREDIT SCORING Provided more accurate credit pricing so that risk premia reflected borrower’s risk characteristics Led to credit staff becoming more sales focused than credit focused 8 BEHAVIOURAL ASPECTS OF CREDIT SCORING Early resistance to impersonal credit scoring techniques Traditional ‘relationship’ approach to lending became too expensive Became much more widely accepted after spectacular judgment-based lending failures during the 1980s * Characteristics of lending failures during the 1980s: https://en.wikipedia.org/wiki/Savings_and_loan_crisis 9 THE IMPERATIVE FOR CREDIT SCORING Significant improvements in credit scoring allowed: Better risk identification within the loan portfolio Improved targeting of client groups Increased loan volume with lower costs Reduction in time for loan decisions Rigorous fine-tuning of loan decisions 10 STATISTICAL CREDIT SCORING VERSUS JUDGMENT METHODS The shift from a more qualitative to quantitative approach reveals: Better use of information including better determination of what are relevant data Easier to access high-volume lending Reasons for the default of classes of borrowers can be more readily determined Improved management control over the loan portfolio’s performance and methods employed in future credit decisions 11 STATISTICAL DECISION-MAKING IN CREDIT SCORING MODELS Statistical decision-making models quantitatively model risk and uncertainty to give a picture of future probabilities. Hoyland (1997) identified thirteen methods used in statistical decision-making models. We will list some of these methods. 12 STATISTICAL DECISION-MAKING IN CREDIT SCORING MODELS 1 – Probability Modelling This modelling aims to predict the future value of cash flows emanating from the firm Identifies controllable (e.g. credit risk position) and uncontrollable factors (e.g. interest rates) to create loan’s risk profile 2 – Credit Application Scorecard Analyses historical data of the applicant and give him a credit score using a scorecard. Fails to be forward-looking Case study: A home loan credit scorecard 13 STATISTICAL DECISION-MAKING IN CREDIT SCORING MODELS 3 – Logistic Regression Allows direct estimation of probabilities by permitting nonlinear model estimation by the use of interpolation or iterative processes 4- Decision Tree Models Categorises the attributes of a client from most to least important until sufficient branches allow for reasoned decision 14 STATISTICAL DECISION-MAKING IN CREDIT SCORING MODELS 5– Genetic Algorithms Evolutionary approach relying on Artificial Intelligence (AI) dealing directly with its environment and able to control for events such as changes in interest rates or macroeconomic variables. 6 – Expert Systems Computer-based decision-tree support systems incorporating an information module, information database module and a learning model 15 CREDIT APPLICATION SCORECARD Definition It is a tool used to calculate a credit score when a customer applies for a new credit. It predicts the probability that the customer is going to default on the loan. 1- Credit scores calculated by the financial institution Each financial institution usually calculate credit scores on applications to new loans using its own systems. 2- Credit scores calculated by external entities In the UAE, Etihad Credit Bureau is mandated by the UAE government to regularly collect credit information from financial and non-financial institutions in the UAE. His mission is to aggregate and analyze this data to calculate Credit Scores and produce Credit Reports that are made available to individuals and companies in the UAE. https://aecb.gov.ae/credit-score https://gulfnews.com/business/banking/individual-credit-scoring-system-toempower-uae-banks-and-customers-1.2015801 16 CASE STUDY AND APPLICATIONS M-SCORE (BENEISH, LEE NICHOLS 2013, FINANCIAL ANALYSTS JOURNAL) LINK: HTTPS://APPS.KELLEY.IU.EDU/BENEISH/MSCORE/MSCOREINPUT • • • • M-SCORE F-SCORE O-SCORE Z-SCORE 17
