Diploma in Consumer Credit Risk Management

Unit 03 Credit Scoring: Principles and Monitoring

Element 3.1: Cut-offs

Learning Outcomes:

What the candidate must do:

3.1.1 Discuss alternative cut-offs

3.1.2 Calculate the optimal cut-off

3.1.3 Set a profit based cut-off

What the candidate must know:

3.1.3 Options for setting a cut-off

3.1.4 Why marginal and overall quality is important

3.1.5 How to calculate breakeven odds

Element 3.2: Profiles and distributions

Learning Outcomes:

What the candidate must do:

3.2.1 Predict the impact on acceptance rate

3.2.2 Identify the type of shift and potential problem

3.2.3 Calculate the degree of stability

What the candidate must know:

3.2.4 How to identify shifts in profile

3.2.5 How to calculate Stability Index

3.2.6 What constitutes a significant shift

Element 3.3: Characteristic analysis

Learning Outcomes:

What the candidate must do:

3.3.1 Calculate Information Value

3.3.2 Identify good and bad characteristics

3.3.3 Determine how to handle dominant characteristics

What the candidate must know:

3.3.4 The stages of variable selection

3.3.5 How to calculate Information Value

3.3.6 What constitutes a good characteristic

3.3.7 How to influence the scorecard construction

Element 3.4: What makes a good scorecard

Learning Outcomes:

What the candidate must do:

3.4.1 Discuss what makes a good scorecard

3.4.2 Calculate a swap set

3.4.3 Discuss what could cause a large swap

What the candidate must know:

3.4.4 How Gini is calculated

3.4.5 Issues with statistical measures

3.4.6 How to read a swap set analysis

Element 3.5: Reject inference

Learning Outcomes:

What the candidate must do:

3.5.1 Discuss why we infer performance

3.5.2 Discuss the merits of alternative methods

3.5.3 See the effect of reject inference on Gini statistics

What the candidate must know:

3.5.4 Why reject inference is necessary

3.5.5 Alternative methods of reject inference

3.5.6 What questions to ask to investigate the effect of the reject inference

Element 3.6: Tracking scorecards: bad rates

Learning Outcomes:

What the candidate must do:

3.6.1 Review a dynamic delinquency report

3.6.2 Calculate the expected bad rate

3.6.3 Read a tracking report and identify the performance shift

What the candidate must know:

3.6.4 Why scorecards are tracked

3.6.5 How to predict the bad rate

3.6.6 How to reassess the expected bad rate from early performance

Element 3.7: Tracking scorecards: characteristics

Learning Outcomes:

What the candidate must do:

3.7.1 Discuss why we drill down

3.7.2 Review a characteristic odds report

3.7.3 Calculate misalignment

What the candidate must know:

3.7.4 The types of characteristic analysis

3.7.5 How to interpret characteristic analyses

3.7.6 The importance of score-odds alignment

Element 3.8: Scorecard Validation

Learning Outcomes:

What the candidate must do:

3.8.1 Discuss why we validate scorecards

3.8.2 Test the significance of attribute groups

3.8.3 Discuss development issues

What the candidate must know:

3.8.4 Why scorecards should be checked

3.8.5 What tests to perform

3.8.6 What might be suspicious