Chief Mathematician, Bank of Montreal, Toronto, Canada. Published Papers in Top Mathematical Journals in US, UK, Canada, Germany, Israel, Australia

Transcription

1 About the Presenter Chief Mathematician, Bank of Montreal, Toronto, Canada. 40+ Published Papers in Top Mathematical Journals in US, UK, Canada, Germany, Israel, Australia Area of Expertise: from Non-associative, Lie Algebras, Group Theory to Marketing, Risk, Credit and Pricing Optimization. 20+ Years of Experience in Financial Industry. 1

2 Customer Credit and Pricing Optimization Yuri Medvedev Bank of Montreal 2

3 The Objective of the Presentation To outline our Credit and Pricing Optimization methodology. How to rethink pricing strategies. What does an optimal solution look like? 3

4 Customer credit pricing optimization problem 4

5 The Structure of the Presentation A Case Study: XYZ-Pricing Solution History Review on Effect/Uplift Modeling Effects and Effect/Uplift Modeling Decision Tree Effect Modeling SAS Macro Effect or Sensitivity Score Examples Effect Lift Charts, Compare/Assess Effect Models Effect Modeling and Credit/Price Optimization The Simulation Box SAS Macro 5

6 A Case Study: XYZ-Pricing Solution at Adjudication. Portfolio: Unsecured Credit Lines, XYZ-pricing solution vs BMO standard prices. The experimental design to measure XYZ-Pricing Benefits. Routed 50/50 through XYZ and BMO standard solution XYZ-prices were about 1% higher than BMO. 6

7 About 1% less accounts were booked via XYZ-Strategy Booking Rates: BMO vs XYZ BMO XYZ 7

8 Accounts booked via XYZ-Strategy had lower balances Balance/Offer: BMO vs XYZ BMO XYZ 8

9 Overall, the XYZ-Strategy reduced revenue Cumulative Revenue/Offer: BMO vs XYZ BMO XYZ 9

10 How to rethink pricing strategies 10

11 History Review on Effect/Uplift Modeling Radcliffe, N. J. and Surry, P. D. (1999). Differential Response Analysis. Lo, V. (2002). The True Lift Model. Larsen, K. (2010). Net Lift Models The author 2001: The Effect Modeling Algorithm and SAS Macro : The Optimization Methodology Developed and Tested. Binary Targets vs Continuous. 11

12 Effects and Effect/Uplift Modeling Credit Limit Increase Test Portfolio: Unsecured Credit Lines (LC), Experimental Design: 60/40 12

13 Effects and Effect/Uplift Modeling Effect is a segment level metric. Effect modelling allows to drill down to the segment and find Sub-segments that generate very significant effects Sub-segments that do not utilize or can be upset by the treatment. Credit Limit Increase balance effect 13

14 Effects and Effect/Uplift Modeling The standard two-way effect model or the difference score model (K. Larsen, 2010). Develop a model on the treatment population: B T = B T X. Develop the second model on the control population: B C = B C X. Effect(X) = B T X B C X The Effect Modeling Challenges High nonlinearity of effects. The effect represents only a fraction of the target metric. Double variance causes unnecessary high model volatility. Business interpretation. 14

15 Decision Tree Effect Modeling SAS Macro Decision Tree Effect Modeling Algorithm quantifies segment level effects. Decision Tree Effect Modeling SAS Macro developed in The Weighted Power Function identifies best splits. The Weight Function allocates higher values for splits that are closer to 50%. 15

16 Decision Tree Effect Modeling SAS Macro The confidence level and the weight function can be adjusted. There is an option to run the macro just maximizing the difference in effects between children nodes. There is an option to identify the list of effect important variables first. It can be used to train conventional decision tree models in SAS. 16

17 Effect or Sensitivity Score Examples Critical Effect Scores for Credit and Pricing Optimization Revenue/Balance/Losses Credit Limit Increase and Interest Rate Decrease Effect/Sensitivity Scores. Revenue/Balance/Losses Credit Limit Decrease and Interest Rate Increase Effect/Sensitivity Scores. Sophisticated sensitivity scores can be developed for combinations of treatments. 17

18 Credit Limit Increase Effect Score on Unsecured Credit Lines Effect performance by cumulative revenue effect score segments 18

19 Interest Rate Increase Effect or Sensitivity Score Portfolio: Unsecured Credit Lines. Treatment: 100 bps Interest Rate Increase. Experimental Design: 50/50 Effect Performance by Cumulative Revenue Effect Score Segments. 19

20 Cumulative Effect Lift Chart, Compare/Assess Effect Models Interest Rate Increase(IRI) Effect/Sensitivity Score Four final effect nodes with score values: 282, 66, 62 and -54. Sort the population from highest-to-lowest effect scores. The y-axis shows the total effect on the x-percentage population. 20

21 Compare/Assess Effect Models Cumulative Effect Chart: IRI vs XYZ scores IRI Effect Model Power is $0.47MM IRI XYZ Random and XYZ power is $0.4MM 21

22 Case Study: XYZ-Pricing Solution. Higher XYZ prices. Cumulative Revenue Effect Chart. The Effect Modeling turns the negative test into a positive business solution. Targeting 57% of the population with higher XYZ- Prices and keeping the rest 43% at lower standard prices would generate additional $1.6MM in revenue effect. Overall Negative Revenue Effect. 22

23 What does an optimal solution look like? 23

24 Effect Modeling and Credit/Price Optimization Effect modeling and the optimization. The limited performance window for effect score segments. Lifetime effect projections on profit and its major components. 24

25 The Simulation Box SAS Macro Models Effect Projections Objective is to provide healthy and competitive pricing of balances. Building SAS Simulation Box is now a standard process. Existing data allows to generate projections for up to 10 years. Required Effect Score and their Effect Curves Inputs: Revenue Cost Loss 25

26 Three phases of the balance effect and its fundamental shape Balance effect Leveling-off months 10 years 26

27 Credit Cards CLI Test: No Signs of Declining Phase The leveling-off phase Balance effect building phase 27

28 Thank you for listening! Any Questions? Session ID: