Prepared by John Anderson, Queensland University of Technology

Credit Scoring Techniques Chapter Three Credit Scoring Techniques

Learning Objectives List the development of credit scoring techniques Discuss the behavioural aspects of credit scoring Explain the imperative for credit scoring

Learning Objectives 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

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

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

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.

The Development of Statistical Credit Scoring Statistical developments in the 1930s–1940s allowed identification of good/bad loans Significant growth in post-WWII consumer credit such as credit cards Provided a non-emotive, rigorous and statistically valid method for determining credit decisions

The Development of Statistical Credit Scoring Computing technology in 1980s allowed development of sophisticated credit scoring methods 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

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

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

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

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 main methods used in statistical decision-making models.

Statistical Decision-Making In Credit Scoring Models 1 – Probability Modelling This modelling aims to predict the future value of cashflows emanating from the firm Identifies controllable (e.g. credit risk stance) and uncontrollable factors (e.g. interest rates) to create loan’s risk profile 2 – Application Credit Scoring Models Analyses historical loan decisions and compares those to estimated default rates Fails to be forward-looking

Statistical Decision-Making In Credit Scoring Models 3 – Application Derivatives (a) Mail Solicitation Score — Modelling the success of cross-selling mail promotions (b) Attrition Models — Predicts the success of products over the product’s lifecycle (c) Authorisation Scores — Determining types and levels of access to funds (particularly credit levels) via mechanisms such as EFTPOS

Statistical Decision-Making In Credit Scoring Models 4 – Judgemental Credit Scoring An estimated model is employed when new products introduced and less than three years data available 5 – Collection Models Specialist models employed to determine best strategy once loan in default, such as ruthless early term follow-up, early warning leading, to total debt write-off

Statistical Decision-Making In Credit Scoring Models 6 - Regression Analysis Use of this linear statistical technique to identify simple ratios, such as age to probability of default, to determine risk levels 7 – Logistic Regression Allows direct estimation of probabilities by permitting nonlinear model estimation by the use of interpolation or iterative processes

Statistical Decision-Making In Credit Scoring Models 8 - Decision Tree Models Categorises the attributes of a client from most to least important until sufficient branches allow for reasoned decision 9 – Neural Networks Relatively new assumption-free approach to credit modelling that ‘learns’ from a training data set to identify characteristics of defaulting loans

Statistical Decision-Making In Credit Scoring Models 10 – 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 11 – Mortality Models Assesses actual bond repayment histories to determine the appropriate level of risk premium required for corporate debt

Statistical Decision-Making In Credit Scoring Models 12 – Chi-Square Automatic Interaction Detector (CHAID) Determines best predictor of an event and splits these into two groups. This process is repeated on these two groups and so on 13 – Expert Systems Computer-based decision-tree support systems incorporating an information module, information database module and a learning model

Social and Ethical Issues in Credit Scoring While credit scoring produces unbiased results, it may: Alienate staff who see themselves as ‘lenders’ and not ‘salespeople’ Increase the perceived impersonal relationship between banker and its staff and clients Produce ‘type I’ and ‘type II’ errors Do lenders occasionally need to think behind the credit score numbers?

Implementing Credit Scoring Within the Organisation A number of steps should be considered when implementing a credit scoring system into an organisation, including: Board and executive management agree on project scope Use of multidisciplinary implementation teams Ensure affected interest groups have input into affected lending areas

Implementing Credit Scoring Within the Organisation Incorporation of existing credit rules to reflect the institution’s values Meticulous training of clerical staff who will assist in creating the lending database Rigorous testing of the database to identify various performance groups, i.e. good v. bad v. rejections Rigorous testing of the draft scoring formulation, with results presented to senior management to strengthen support

Implementing Credit Scoring Within the Organisation Comparing current loan acceptance rates with those produced by the model and fine-tuning of cut-off rates Develop implementation procedures, IT infrastructure and conduct sample implementation Refine credit scoring operational procedures Develop ‘exceptions’ and over-ride rules Implement across lending institution

Implementing Credit Scoring Within the Organisation Ultimately, the Credit scoring system must assess unknown individuals and produce a consistent credit assessment result. The credit scoring process is one that will evolve over time to incorporate new information and be used in other lending activities.