Identifying & Ranking Bank Credit Risk - The Risk Grading Approach -

1. Identifying & Ranking Bank Credit Risk- The Risk Grading Approach - March 2008 Dr. Leonie Lethbridge Head of Risk Strategic Markets ANZ Disclaimer: The views expressed in this presentation are the views of the author and should not be construed as the views of ANZ.

2. ANZ at a glance • Established in 1835 • Strong market positions in chosen markets • Australian “Bank of the Year” six years in a row • New Zealand’s largest bank • Leading bank in the South Pacific • Leading Australian bank in Asia • Over 5 million customers, across 30 countries • 33,800+ employees; 1,200+ points of representation • Strong performance • Annual profit US$2.8 billion • Return on equity 20.7% • Cost/Income ratio 44.6% • Market capitalisation US$41 billion • Rated AA

3. Contents • Modelling and measuring risk • Selecting a risk ranking process for a given situation • Credit Scoring • Understanding the business impact • Managing consequences of misuse • Lessons learnt • Appendix: • An Example of a Scorecard • Scorecard Development Process Flow • In-depth Monitoring can uncover System issues

4. Alan Greenspan:President, Federal Reserve BoardMay 1996 Why? “… We should not forget that the basic economic function of these regulated entities (banks) is to take risk. If we minimise risk taking in order to reduce failure rates to zero, we will, by definition, have eliminated the purpose of the banking system.” The smart way to say “Yes”

5. Risk Appetite Regulatory Capital PDs and LGDs PDs and LGDs PDs and LGDs PDs and LGDs PDs and LGDs PDs and LGDs PDs and LGDs PDs and LGDs PDs and LGDs Model Risk Grading Approach Financial Data and Qualitative Inputs Provisioning Limit Setting Model Algorithms Overrides Expert Knowledge Independent Reviews Out-of-Model Info Processes Portfolio Management Credit Approval Discretions Economic Capital Pricing The Credit Risk Management Framework Performance Monitoring and Reporting Policy Risk Managers need to be aware, be proactive and aim for timely and accurate risk grading.

6. Probability of Default (PD) Risk Grading Models Expected Loss (EL) Loss Given Default (LGD) = PD * LGD * EAD Source Systems Exposure at Default (EAD) Bank Masterscale Customer Rating (internal use) PD/LGD Mapping to External Agency ratings Equivalent Rating (external use) Rating Model Outputs

7. Business Segmentation Complexity $ Involved Number of customers (Data) Segment Large Specialised Banks/Sovereigns/ Project Finance Very High hundreds Institutional Wholesale Business High Corporate thousands Large Business Small Business Moderate Mortgages hundreds of thousands Retail Personal Credit Cards Behavioural High millions Small

8. Selecting a risk ranking process for a given situationThe viability of building a model is driven by data availability but considers other factors as well …. Data Availability Type of Model Other Factors Statistical Model Producing a PD High Volume, High Default Segments Homogenous, large customer segment that may be rated in a decentralised way (e.g. small business loans) RETAIL & WHOLESALE Financials Statistical Modelling Statistical Model Producing a Rating PD CCR Behaviour Non-Financials Hybrid Statistical and Expert Model Specialised customer segment where External Rating Agency expertise abounds, e.g. Sovereigns Adopt Agency Rating Agency Rating Replication Model Expert Model Producing a Rating Highly customised or specialised, smaller customer segment likely to be centrally rated and reviewed (e.g. GOEs) Low Volume, Low Default Segments Risk Grading Policy Guidelines

9. All knowledge Real knowledge Own perception Others perception “Dangerous” ground Courtesy: Prof. Mark Burgman, University of Melbourne The Questions with Expert Systems • How “expert” are the Experts? • Entrenched expert opinion Þ overconfidence in ability Þ “fact” • Experts are susceptible to psychology and motivational bias • Experts can disagree • How to aggregate opinions • Fail to record differences of opinion • Fail to acknowledge misunderstanding

10. Optimisation Strategies ANZ’s Journey to Date Action Based Clusters Dynamic Models Data Driven Strategies Data Volume Needed Static Models Judgmental Strategies Profiling & Segmentation Predictive Models Decision Optimisation Multidimensional Assessment Complexity

11. 3. Credit Scoring What is Credit Scoring? • A statistical means of providing a quantifiable measure for a given customer or applicant based on information available • Types of scoring: • Credit scoring • Marketing scoring (propensity to buy, attrite etc) • Profitability scoring • Operations scoring (Recoveries, Fraud, Bankruptcy etc) • Credit scoring is the assessment of credit risk using models to predict future behaviour, such as days in excess, arrears, default • The process translates information provided into numbers that are added together to arrive at a score • Each score corresponds to a given level of risk for a customer or product

12. Credit Risk Scoring: Benefits of Scoring in Retail • Increase consistency of lending decisions • Consistent & unbiased treatment of applicant • Customers with the same details get the same score and hence same decision • Total management control over credit approval systems • Allows for loosening or tightening of lending through credit cycles • Potential increase in approvals • Reduce operating costs • Increase in automated processing • Improve customer service • Fast and consistent decisions at application point • More appropriate limit and authorisation decisions • Reduction in collection actions on low risk accounts • Risk based allocation of credit limits and issue terms • Quantifiable • A score maps to a predicted default rate

13. Credit Risk Scoring: Benefits of Scoring in Retail (cont.) • Improved portfolio management • Manage credit portfolios more effectively and dynamically • Better prediction of credit losses • Management ability to react to changes fast & accurately • Ability to measure & forecast impact of policy decisions • Quick and uniform policy implementation • Improved Management Information Systems (MIS) • Permits MIS to be developed to assist business needs and marketing activities • MIS can be fed back into future scorecard developments and collection activities • Increase revenue • Faster processing of better customers • Risk can be managed to allow revenue from “lower quality” customers • e.g. Honour/Dishonour ATM fees

14. Scorecard Usage - Retail Objectives Opportunity ü ü ü ü ü ü ü ü ü ü ü ü ü ü ü ü ü ü ü ü ü ü ü ü ü ü

15. Application Scores

16. Character • Time at current employment • Residential status • Time at current address Financial • Assets • Liabilities • Total Monthly income Application score Bureau • No. of bureau defaults • Adverse behaviour Application • Purpose of loan • Deposit, security Application Scoring • Characteristics used in application scoring • New & Existing customers who were accepted or declined • Primary information from application form & credit bureau • Aims to measure the customers willingness to repay, stability factors important

17. Behaviour Risk Scores

18. Behaviour Scoring • Characteristics used in behaviour scoring • Existing customers • Primary information based on historical account conduct, over various time periods • Previous Delinquency • Historically 30 dpd • Historically 60 dpd • Historically 90 dpd • Credit history • Number of payments • Value of payments • Payment patterns over time • early/late • value Behaviour score • Debit history • Number of purchases • Number of cash advances • No. of redraws • Time since last redraw • Other • Utilisation • Age of account

19. Data Quantity sufficient defaults µ Final Model collection process Data Quality Modelling experience & expertise Rating Model Build Process & Lifecycle Source Systems Financials Historical Investigate Model version n+1 Model Build Data N Information on file Y Acceptable ? In Use Model version n Collected Data N Iterative process of ongoing review and improvement! Policy Business Intuition Lending Experience Research Science Requirement for Rating Model Review Full Rebuild Model Build

20. New Risk Grading Process Confusion Discomfort Different Outcomes Change is “bad” Risk Grading Process Development Change Management New Risk Grading Process Active Involvement “Along for the journey” Communication Training Support Empathy 4. Understanding the Business Impact Existing Risk Grading Process Familiarity Comfort Known Outcomes Change is “unnecessary” Issues addressed Feedback accepted Ongoing Monitoring

21. Model error System error Operating error Deliberate error Insufficient oversight 5. Managing the Consequences of Misuse Misuse Further Misuse & Bad Habits PD & LGD wrong Customers defaulting unexpectedly Capital Calculations Incorrect! Recoveries less than expected Incorrect pricing Customer dissatisfaction internal loss of confidence Issues with Regulator $ losses Reputational Risk

22. Avoiding Misuse Basel II (“Basel is just good risk management”) 441. Banks must have independent credit risk control units that are responsible for the design or selection, implementation and performance of their internal rating systems. The unit(s) must be functionally independent from the personnel and management functions responsible for originating exposures. 442. A credit risk control unit must actively participate in the development, selection, implementation and validation of rating models. It must assume oversight and supervision responsibilities for any models used in the rating process, and ultimate responsibility for the ongoing review and alterations to rating models.

23. Avoiding Misuse Validation Model error Monitoring System error Operating error Deliberate error Insufficient review Reporting to Management Oversight

24. ? Example of Operational Monitoring – Retail Decision type & Volume over Time • System with no input checking. Model for less than $250K lending. One application for $25M

25. Example of Operational Monitoring – Wholesale Override Rate over Time • Increase rate: issue with model

26. 6. Lessons Learnt Continuous Involvement Quality data Get business / stakeholder ‘buy-in’ Development Planning & Organisation Planning Implementation Change Management Amend Ongoing Overview Validation In-use Review Processes Monitoring Training Adequacy Communicate Issues to Management Address Issues

27. 7. Appendix

28. An Example of a Scorecard Relative Power 9 10 14 9 18 27

29. Scorecard Development Process Flow

30. In-depth Monitoring can uncover System issues As calculated by SYSTEM • Rounding Error in SYSTEM – largest impact on the score is when Actual Ratio is: • 0.182 to 0.474: Correct score is –19, whereas SYSTEM gives –43 (rounding to 0) • 0.474 to 0.5: Correct Score is –8, whereas SYSTEM gives –43 (rounding to 0) As should be calculated by SYSTEM