Credit Scoring: Data and Decisioning

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Sarah Wilson
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1 Credit Scoring: Data and Decisioning What should scoring do for collections? : Best Practice in Credit Management & Collections Le Meridien Queen s Hotel, Leeds, Wednesday 19 th June 2002 Helen McNab SCOREPLUS 0

2 Credit Scoring, Data and Decisioning Structure of Presentation ScorePlus Data Scoring Group Actions Individual Actions 1

3 What is ScorePlus? Rigourous credit management Analytic base Practical approach Analysis Scorecards Bad debt models Profitability models Consulting Strategy development Credit policy review Collections management Training Scoring management Scorecard development Behavioural scoring Software Call centre management Portfolio forecasting Clients include: Barclays Bank Royal Bank of Scotland The Standard Bank of South Africa Halifax Orange Littlewoods 2

models Consulting Strategy development Credit policy review Collections management Training Scoring management

4 Credit Scoring, Data and Decisioning ScorePlus Data Scoring Group Actions Individual Actions 3

5 Scores structure data Data SCORES D E C I S I O N S Scores consolidate existing data Focus on one aspect of customer behaviour e.g. charge off risk on entering collections Give numerical estimate of expected behaviour 4

6 Collection scores: Measure underlying customer risk Charge off risk Score = probability of charge off Score 5

7 Example data sources: Collection scores Predictive power determined by quality and range of data Data Source Application data Acount history Credit bureau Collections history Calling history Use in collection scores Frequent Frequent Sometimes Negligible Negligible 6

data Acount history Credit bureau Collections history Calling

8 Credit Scoring, Data and Decisioning ScorePlus Data Scoring Group Actions Individual Actions 7

9 Typical collections action path Collections actions over time: severity increases as risk increases 6 Intensity of actions Days since entering collections 8

10 Actions equate to perceived risk 6 Intensity of actions Days since entering collections 9

11 Actions equate to perceived risk Projectory over time for an average case Intensity of actions Charge off risk Days since entering collections 10

12 Actions equate to perceived risk Projectory over time for an average case...dictates tactics timing and type Charge off risk Customer Low risk retention Customer Medium risk rehabilitation High Full balance risk collection and severance Days since entering collections 11

13 Collection scores: identify risk at entry Scores therefore refocus tactics. Charge off risk Score 100 Score 200 Full balance collection and severance Days since entering collections 12

14 Tactics relate to entry risk and time in collections Scores therefore refocus tactics. in terms of risk and time Charge off risk Customer retention Score 200 Score 300 Days since entering collections 13

15 Credit Scoring, Data and Decisioning ScorePlus Data Scoring Group Actions Individual Actions 14

16 Example data sources: Collection scores Data Source Application data Acount history Credit bureau Use in collection scores Collections history Calling history Consequence: score minor component of strategies 15

collection scores Collections history Calling

17 Solution: Campaign-based scores - Model 1: collection action: Score 1 No action - Model 2: collection action: Score 2 Build two score models to test outcomes Captures action and reaction collections history key component 16

Score 2 Build two score models to test outcomes

18 Who-to-call example Account Probability of Payment With a Collection Call Probability of Payment With no Collection Call Charge off risk No. 1 18% 17% No. 2 25% 5% No. 3 30% 15% No. 4 60% 58% vs 17

19 When-to-call example Build contact models for each hour of the day scores identify calling potential Captures action and reaction calling history key component Morning Afternoon Evening Account Account Account

reaction calling history key component Morning Afternoon

20 When-to-call example Build contact models for each hour of the day score identifies calling potential Captures action and reaction calling history key component Morning Afternoon Evening Account Account Account

and reaction calling history key component Morning Afternoon

21 Collection scores Data Source Application data Acount history Credit bureau Collection scores Type Uses Risk at entry Group tactics Collections history Calling history Campaign Individual actions Consequence: score major component of strategies 20

22 Credit Scoring, Data and Decisioning ScorePlus Data Scoring Group Actions Individual Actions 21

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