The 2nd East African Community Regional Credit Information Sharing Conference 24th and 25th September 2013

1. The 2nd East African Community Regional Credit Information Sharing Conference24th and 25th September 2013 How to make lending decisions using credit reports Thamir Hassan Senior Vice President, Business Development TransUnion Africa

2. Agenda • Overview of the Credit Customer Life Cycle • Basic contents of a Credit Report • How Do We Interpret Data • Benefits of Data Analysis • The Basic Principles of Scoring • Summary • Questions

3. The Credit Life Cycle Business decision making is needed in all phases of the Account Life Cycle: • Application processing of through the door applications • Risk Assessment • Line Assignment • Pricing • Account management of the existing credit portfolio • Portfolio Risk Grading • Exposure • Top Up / Line Adjustment • Cross Selling • Attrition • Retention Strategies • Management of seriously delinquent and written-off accounts • Early Collections • Debt Provisioning 3

4. Lending Decisions • Either Accept or Decline the application • For our Accept Decisions, we hope for minimum bad debt • For our Decline Decisions, we hope we did not turn away good business • There are no PERFECT decisions! Simple outcome but difficult decision: 4

5. Lending Decisions • The more effective the lending decisions, the better it differentiates between bad and good accounts Objective: Minimise Overlap Area Maximisethe “separation” between good and bad accounts GOOD ACCOUNTS BAD ACCOUNTS Accept Decline OVERLAP AREA 5

6. What Do We Rely on to Make Our Decisions • Demographic information of the applicant • Affordability • Purpose of the loan • Macro economics • Other ..... • Credit Report Besides long years of experience: 6

7. The Basic Contents of a Credit Report • Header information about the individual • Summary of accounts and activity • Details of non-performing accounts • Details of enquiries • Bureau Negative Score An example from TransUnion Credit Bureau in Kenya (Negative data only) 7

8. The Basic Contents of a Credit Report • Header information about the individual • Summary of accounts and activity • Details of payment profile per account held covering up to 24 months • Details of enquiries during the last 24 months • Details of Judgments, Defaults, Notices • The more data the better An example from TransUnion Credit Bureau in South Africa (Positive and Negative data) 8

9. How Do We Become More Affective in Interpreting the Information in the Credit Report? • Data Analysis • Data Analysis • Data Analysis Besides long years of experience, again!: 9

10. How Does Data Analysis Help? Let us consider an example of analysing Bad Accounts by Age Band 10

11. How Does Data Analysis Help? Let us consider an example of analysing Bad Accounts by Age Band 11

12. How Does Data Analysis Help? Let us consider an example of analysing Good and Bad Accounts by Age Band ? 12

13. How Does Data Analysis Help? We need to look at the Bad Rate 13

14. Data Analysis of Credit Report Information Data sources used for statistical Analysis Creation Process Data Sources Data Components Credit Characteristics Aggregated credit profile data divided into industries and categories. These make up the building blocks of generic scoring models Credit grantors Data elements collected from various data sources are referenced by computer programs Consumer indicative information Public domain Default Judgment Debt review Notice TransUnion databases Enquiries Payment profile information

15. Data Analysis of Credit Report Information Type of Credit Characteristics • DemographicInformation • Long / ShortCredit History Recency / Stability Delinquency • DelinquencyRecency /Frequency /Magnitude • Thick / ThinCredit File • Product Type:Secured / Unsecured / Revolving / etc Potential Candidates for Credt Characteristics • Enquiryrecency /frequency High / Low, Increasing /DecreasingUtilisation Activity Exposure High / Low, Increasing /DecreasingExposure 15

16. Data Analysis of Credit Report Information Advantages of statistical Analysis • Based on reality rather than perception • Capture and highlight new trends • Able to utilise various sources of data • BUT • How do we manage to learn from this vast data?

17. The Basic Principles of Scoring Evaluation date Behavioural period Performance period April 2011 March 2012 March 2013 12 Months learning period Providing us with the INPUT VARAIABLES Known outcomes in the next 12-month period • The performance during the performance months is linked via a statistical formula to the behaviours during the behaviour months LEARN All Data is in the PAST

18. The Basic Principles of Scoring Evaluation date Behavioural period Performance period July 2012 June 2013 June 2014 Predicted outcomes in the next 12-month period • Apply the statistical formula to predict future behaviour APPLY Obtain input variables from the 12 month period

19. The Basic Principles of Scoring • An example of a Scorecard formula

20. The Basic Principles of Scoring An example of an Account Acquisition Score • The Score enables the grouping or banding of consumers according to the risk associated with them • A cut-off score associated to the risk of the consumers can now be set according to the creditor’s acceptance and risk policy • Example: A consumer who scores less than 501 will not be accepted, since the risk associated with the consumer is undesirable • Credit risk control can be applied by Management to: • Increase the acceptance rate • Decrease the bad rate • Combination of both 20

21. The Basic Principles of Scoring An example of an Account Management Score • The score enables the grouping or banding of consumers according to the risk associated with them • The groupings enable more effective account management Increase credit limit by 40% Increase credit limit by 30% Increase credit limit by 15% Increase credit limit by 5% No credit limit increases 21

22. How to Make Lending Decisions Using Credit Reports? • Create Credit Characteristics out of the credit report data • Analyse these Characteristics against Bad Rate • Rank them in terms of their strength to relate to bad rate • Utilise statistics to build scorecards SUMMARY: Besides long years of experience : 22

23. Q & A Email: thassan@transunion.co.za 23