2011 advanced analytics through the credit cycle

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Presentation at the SAS Analytics Conference 2011, Orlando, FL.

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Alejandro Correa Bahnsen
Andres Felipe Gonzalez Montoya

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2011 advanced analytics through the credit cycle

  1. 1. Advanced Analytics through the credit cycle Alejandro Correa B. Andrés Gonzalez M.Copyright © 2011, SAS Institute Inc. All rights reserved. #analytics2011
  2. 2. Introduction PRE- ORIGINATION Credit Cycle POST- ORIGINATION ORIGINATION Copyright © 2011, SAS Institute Inc. All rights reserved. #analytics2011
  3. 3. Introduction Up sell Cross sell Credit limit Credit limit Behavior Portfolios Fraud Fraud Free fall Churn Income Origination Recovery Identification CollectionPropensityPre-Origination Origination Maintenance Collection Copyright © 2011, SAS Institute Inc. All rights reserved. #analytics2011
  4. 4. Copyright © 2011, SAS Institute Inc. All rights reserved. #analytics2011
  5. 5. Pre-Origination Propensity ModelsWhat is it?  A propensity model is a statistical scorecard that is used to predict the acceptance behavior of a prospect client.What is sought?  Compute the probability that a prospect client accepts an offered product. Copyright © 2011, SAS Institute Inc. All rights reserved. #analytics2011
  6. 6. Pre-Origination Propensity ModelsObjectives  Classify prospect clients into high propensity and low propensity.  Focus efforts on costumers who are more likely to accept one of the regular products.  Identify the profile of clients with a low propensity score and design tailor made products. Optimize: Increase the acceptance and decrease efforts. Copyright © 2011, SAS Institute Inc. All rights reserved. #analytics2011
  7. 7. Pre-Origination Propensity ModelsVariables  Bureau: Credit behavior information.  Demographic: Personal information. Credit Experience Gender City Buerau Inquiries Marital Status Delinquencies Credit Limit Education Quantity of C.C. Current Products Age Copyright © 2011, SAS Institute Inc. All rights reserved. #analytics2011
  8. 8. Pre-Origination High Propensity ModelsMultiple offer Propensity to accept Single offer Tailor made products Low Propensity to accept Copyright © 2011, SAS Institute Inc. All rights reserved. #analytics2011
  9. 9. Pre-Origination Profile AnalysisPropensity vs Risk Acceptance Rate Bureau Score Propensity Score Low Medium High Low 23.65% 31.05% 49.42% Medium 63.75% 65.61% 75.47% High 83.69% 85.80% 87.36% Offer Regular products Copyright © 2011, SAS Institute Inc. All rights reserved. #analytics2011
  10. 10. Pre-Origination Profile AnalysisPropensity vs Risk Acceptance Rate Bureau Score Propensity Score Low Medium High Low 23.65% 31.05% 49.42% Medium 63.75% 65.61% 75.47% High 83.69% 85.80% 87.36% Offer Tailor Regular made products products Copyright © 2011, SAS Institute Inc. All rights reserved. #analytics2011
  11. 11. Pre-Origination Profile AnalysisCluster analysis  Create groups between objects that are more similar to each other than to those in other clusters.Objectives  Characterize a population.  Understand behaviors.  Identify opportunities.  Apply differential strategies. Copyright © 2011, SAS Institute Inc. All rights reserved. #analytics2011
  12. 12. Pre-Origination Profile AnalysisCluster analysis Copyright © 2011, SAS Institute Inc. All rights reserved. #analytics2011
  13. 13. Pre-Origination ResultsHigh/Medium Propensity (Product Acceptance) 23.110% 24.000% Increase: 18% 23.000% 22.000% 19.580% 21.000% 20.000% 19.000% 18.000% 17.000% With propensity model Without propensity model Copyright © 2011, SAS Institute Inc. All rights reserved. #analytics2011
  14. 14. Pre-Origination ResultsHigh/Medium Propensity (Product Acceptance) 23.110% 24.000% Acceptance Rate Bureau Score Propensity Score 23.000% Low Medium High Low 23.65% 31.05% 49.42% 22.000% Medium 63.75% 65.61% 75.47% 21.000% High 83.69% 85.80%19.580% 87.36% 20.000% 19.000% 18.000% 17.000% With propensity model Without propensity model Copyright © 2011, SAS Institute Inc. All rights reserved. #analytics2011
  15. 15. Pre-Origination Results Copyright © 2011, SAS Institute Inc. All rights reserved. #analytics2011
  16. 16. Pre-Origination ResultsPROFILE 1 PROFILE 2 PROFILE 3Response Response Response Accept Don´t Accept Don´t AcceptGender Gender Gender Female Female MaleAge Age Age 56 Years or more 22 to 45 Years 36 Years or moreUp to date Active Obligations Up to date Active Obligations Up to date Active Obligations 2 or less 3 to 7 More than 5Number or Mortgage Credits Number or Mortgage Credits Number or Mortgage Credits None None 1 or moreNumber of total Credit Cards Number of Credit Card Number of Credit Cards 0 or 1 C.C. 2 or 3 C.C. More than 3 C.C.Average Credit Card Limits Average Credit Card Limits Average Credit Card Limits 0 Less than US$4.000 More than US$4.000Average Credit Card Utilization Average Credit Card Utilization Average Credit Card Utilization 0% More than 9% 1% to 37%Approved Credit limit in Colpatria Approved Credit limit in Colpatria Approved Credit limit in Colpatria Less than US$450 US$450 to US$1.500 More than US$1.500Currently Active Checking Accounts Currently Active Checking Accounts Currently Active Checking Accounts None None 1 or moreCurrently Active Saving Accounts Currently Active Saving Accounts Currently Active Saving Accounts None 1 2 or moreOffered Credit Card Offered Credit Card Offered Credit Card Visa Clasic Visa Clasic Visa Gold and Platinum Mastercard Clasic Mastercard Clasic Mastercard Gold and Platinum Copyright © 2011, SAS Institute Inc. All rights reserved. #analytics2011
  17. 17. Pre-Origination ResultsPROFILE 1 PROFILE 2 PROFILE 3Response Response Response Accept Don´t Accept Don´t AcceptGender Gender Gender Female Female MaleAge Age Age 56 Years or more Acceptance 22 to 45 Years Rate 36 Years or moreUp to date Active Obligations Up to date Active Obligations Up to date Active Obligations 2 or less 3 to 7 Bureau Score More than 5 Propensity ScoreNumber or Mortgage Credits Low Medium Number or Mortgage Credits High Number or Mortgage Credits None None 1 or moreNumber of total Credit Cards Low Number of Credit Card 31.05% 23.65% 49.42% of Credit Cards Number 2 or 3 C.C. 0 or 1 C.C. Medium 63.75% 65.61% 75.47% than 3 C.C. MoreAverage Credit Card Limits Average Credit Card Limits Average Credit Card Limits 0 High 83.69% Less than US$4.000 85.80% 87.36% than US$4.000 MoreAverage Credit Card Utilization Average Credit Card Utilization Average Credit Card Utilization 0% More than 9% 1% to 37%Approved Credit limit in Colpatria Approved Credit limit in Colpatria Approved Credit limit in Colpatria Less than US$450 US$450 to US$1.500 More than US$1.500Currently Active Checking Accounts Currently Active Checking Accounts Currently Active Checking Accounts None None 1 or moreCurrently Active Saving Accounts Currently Active Saving Accounts Currently Active Saving Accounts None 1 2 or moreOffered Credit Card Offered Credit Card Offered Credit Card Visa Clasic Visa Clasic Visa Gold and Platinum Mastercard Clasic Mastercard Clasic Mastercard Gold and Platinum Copyright © 2011, SAS Institute Inc. All rights reserved. #analytics2011
  18. 18. Pre-Origination ResultsLow Propensity (Product Acceptance) Increase: 77% 18.940% 20.000% 17.060% Increase: 200% 18.000% 16.000% Increase: 50% 14.000% 9.630% 12.000% 7.680% 10.000% 6.250% 8.000% 5.130% 6.000% 4.000% 2.000% .000% Profile 1 Profile 2 Profile 3 Tailor made product Regular product Copyright © 2011, SAS Institute Inc. All rights reserved. #analytics2011
  19. 19. Copyright © 2011, SAS Institute Inc. All rights reserved. #analytics2011
  20. 20. Origination Advance Strategies Flow Product Selection Initial Portfolio offer Association Rules Diferential Scorecard Predictive Clusters Copyright © 2011, SAS Institute Inc. All rights reserved. #analytics2011
  21. 21. Origination Advance StrategiesPredictive Cluster 3.3 3.7 6.5 8.9 Copyright © 2011, SAS Institute Inc. All rights reserved. #analytics2011
  22. 22. Origination Advance StrategiesPredictive Cluster Copyright © 2011, SAS Institute Inc. All rights reserved. #analytics2011
  23. 23. Origination Advance Strategies Diferential Scorecards PROFILE 1 SCORE 1CLASSIFICATION PROFILE 2 SCORE 2 MODEL PROFILE 3 SCORE 3 Copyright © 2011, SAS Institute Inc. All rights reserved. #analytics2011
  24. 24. Origination Advance StrategiesAssociation Rules Understand the behavior of clients based on transactions:  Dates of acquisition.  Products acquired. Find the most commonly product acquisition patterns:  Costumer maturity. Empty Nest  Product grade. Investment, travel Growth of childrenSupport (x,y): Number of times that appears the combination (x,y) / Total Transaction Buy home and meet family needs Young Savings for future purchases Copyright © 2011, SAS Institute Inc. All rights reserved. #analytics2011
  25. 25. Origination Advance StrategiesAssociation Rules Understand the behavior of clients based on transactions:  Dates of acquisition.  Products acquired. Find the most commonly product acquisition patterns:  Costumer maturity.  Product grade. 4 Empty Nest Investment, travel 3 Growth of children college and Retirement.Support (x,y): Number of times that appears the combination (x,y) / Total Transaction 2 Newlywed Buy home and meet family needs 1 Young Savings for future purchases Copyright © 2011, SAS Institute Inc. All rights reserved. #analytics2011
  26. 26. Origination Advance StrategiesAssociation Rules Understand the behavior of clients based on transactions:  Dates of acquisition.  Products acquired. Find the most commonly product acquisition patterns:  Costumer maturity.  Product grade. 4 Empty Nest Mortgage Investment, travel 3 Growth of children Vehicule college and Retirement.Support (x,y): Number of times that appears the combination (x,y) / Total Transaction 2 Newlywed P-loan Buy home and meet family needs 1 Young Savings for future purchases Credit Card Copyright © 2011, SAS Institute Inc. All rights reserved. #analytics2011
  27. 27. Origination Advance StrategiesAssociation Rules Results Support: C.C. C.C. 28.56% Support: C.C. P-loan 16.22% Support: C.C. C.C. P-loan 12.61% Copyright © 2011, SAS Institute Inc. All rights reserved. #analytics2011
  28. 28. Origination Advance StrategiesPortfolio Offer Association Rules Diferential Risk Models Classification Model Portfolio Offer Copyright © 2011, SAS Institute Inc. All rights reserved. #analytics2011
  29. 29. Origination Advance StrategiesInitial Portfolio Offer Remaining Income Product A Monthly Installment is divided in number of Montly Installment Client Income products according to Associationusing Calculated Rules Product B client risk and profile Model Product C Debt Copyright © 2011, SAS Institute Inc. All rights reserved. #analytics2011
  30. 30. OriginationPortfolio Selection Copyright © 2011, SAS Institute Inc. All rights reserved. #analytics2011
  31. 31. Origination Advance StrategiesPortfolio Selection Product A Client declined Product C Product B Product C Copyright © 2011, SAS Institute Inc. All rights reserved. #analytics2011
  32. 32. Origination Advance StrategiesPortfolio Selection Product A Client want more credit limit on Product A Product B Copyright © 2011, SAS Institute Inc. All rights reserved. #analytics2011
  33. 33. Copyright © 2011, SAS Institute Inc. All rights reserved. #analytics2011
  34. 34. Post-Origination Maintenance Traditional behavior strategies Policies Behavior Score  What about Profitability? Current  Attrition? Products Offers Copyright © 2011, SAS Institute Inc. All rights reserved. #analytics2011
  35. 35. Post-Origination Maintenance Behavior Model Historic Variables + Demographic Variables + Bureau Variables Days Past Due Observation Month1 Month 2 Month T Behavior Point Y = maximum dpd over performance window  Forecast client loan behavior using its past behavior Copyright © 2011, SAS Institute Inc. All rights reserved. #analytics2011
  36. 36. Post-Origination Maintenance Profitability Model Historic Variables + Demographic Variables + Bureau Variables Profitability Observation Month1 Month 2 Month T Behavior Point Y = Cumulative profitability over performance window  Forecast client profitability using its past behavior Differences Between Models  A good behavior score does not necessary mean a good profitability Copyright © 2011, SAS Institute Inc. All rights reserved. #analytics2011
  37. 37. Post-Origination Maintenance Attrition Model Historic Variables + Demographic Variables + Bureau Variables Attrition Observation Month1 Point Y = Clients Attrition over the performance window  Client Probability of attrition over next T months Differences Between Models  A client may be profitable but how to know wish ones are more likely to leave Copyright © 2011, SAS Institute Inc. All rights reserved. #analytics2011
  38. 38. Post-Origination Maintenance Solution  Develop an index that combine clients Behavior, Profitability and Attrition Scores  CLIDI (Client Limit Increase Decrease Index) Copyright © 2011, SAS Institute Inc. All rights reserved. #analytics2011
  39. 39. Post-Origination Maintenance High Profitability Score CLIDI vs Profitability Score High Attrition ScoreHigh Profitability Score vs High Behavior Score Attrition Score High Behavior Score vs High Attrition Score Behavior Score Copyright © 2011, SAS Institute Inc. All rights reserved. #analytics2011
  40. 40. Post-Origination Maintenance New behavior strategy Profitability Score + Attrition Score + Risk Score = CLIDI  The CLIDI Index is the weighted average of the 3 scores. Copyright © 2011, SAS Institute Inc. All rights reserved. #analytics2011
  41. 41. Post-Origination Maintenance New behavior strategy Profitability Score  Clients that receive the Policies Attrition CLIDI offer are the best in Score terms of behavior score and profitability score Credit Current  Also strategies are Products card develop to decreased Behavior Model good clients attrition Offers Copyright © 2011, SAS Institute Inc. All rights reserved. #analytics2011
  42. 42. Post-Origination CLIDI distribution New behavior strategy Agresive Average CLIDI Strategies 10 46 52 57 62 66 69 73 77 80 82 9 42 48 55 59 63 67 71 74 77 79 Behavior Score 8 38 45 52 57 61 65 68 71 73 75 7 34 42 49 54 59 62 66 69 70 71 6 32 40 47 52 56 60 63 66 67 68 5 30 37 44 49 53 57 60 63 63 64 4 27 34 41 45 49 53 57 59 60 61 3 24 32 38 42 46 50 53 56 57 58 2 22 29 34 38 42 46 50 53 55 58 1 20 26 31 35 39 43 47 51 53 57 1 2 3 4 5 6 7 8 9 10 Profitability Score No Strategy Taylor made Strategies (Control Groups) Copyright © 2011, SAS Institute Inc. All rights reserved. #analytics2011
  43. 43. Post-Origination How to increase Models Predictive Power?  New Variables  Slope  R2  New Models  Neural Networks  Ensemble Models Copyright © 2011, SAS Institute Inc. All rights reserved. #analytics2011
  44. 44. Post-Origination Variables Traditional behavior variables Variable Calculation Time Purchases Sum, Max, Average, Count 3, 6, …, 24 months DPD Count, Max, Min, Average, Standard 3, 6, …, 24 months Deviation Utilization Max, Min, Average, Standard Deviation 3, 6, …, 24 months Collections Sum, Count, Standard Deviation, 3, 6, …, 24 months Average, Response New behavior variables  Slope and linear regression R2. Copyright © 2011, SAS Institute Inc. All rights reserved. #analytics2011
  45. 45. Post-Origination Variables Example 100.00% Statistic Client 1 Client 2 90.00% 80.00% Average 56% 56% 70.00% 60.00% Std 22% 22% Utulization Client 1 50.00% Client 2 40.00% Min 19% 20% 30.00% 20.00% Max 91% 91% 10.00% Slope 11% -10% .00% 1004 1001 1002 1003 1005 1006 1007 1008 1009 1010 1011 1012 Month Traditional variables are the same for both clients Copyright © 2011, SAS Institute Inc. All rights reserved. #analytics2011
  46. 46. Post-Origination Variables Example 90.000% Statistic Client 1 Client 2 80.000% 70.000% Average 37% 35% Client 1 60.000% Client 2 Std 23% 23%Utilization 50.000% Min 4% 4% 40.000% 30.000% Max 75% 79% 20.000% Slope -17% -16% 10.000% R2 99% 76% .000% 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 Month Traditional variables are the same for both clients Copyright © 2011, SAS Institute Inc. All rights reserved. #analytics2011
  47. 47. Post-Origination Variables Linear regression slope DPD’s last 12 months Linear regression slope DPD’s last 6 months Low correlation between 12 a 6 months slope’s! Copyright © 2011, SAS Institute Inc. All rights reserved. #analytics2011
  48. 48. Post-Origination How to increased Models Predictive Power?  New Variables  Slope  R2  New Models  Neural Networks  Ensemble Models Copyright © 2011, SAS Institute Inc. All rights reserved. #analytics2011
  49. 49. Post-Origination Neural Networks Mathematical model that tries to imitate a biological neuron. Consist in tree parts: Input Layer; Hidden Layer; Target Layer. Input Hidden Target Layer Layer Layer X1 X2 X3 score X4 Bias 1 1 Copyright © 2011, SAS Institute Inc. All rights reserved. #analytics2011
  50. 50. Post-Origination Neural Networks | Copyright © 2011, SAS Institute Inc. All rights reserved. #analytics2011
  51. 51. Post-Origination Neural Networks Why Neural Networks? Pros Cons Interpretability Predictive Power Architecture Selection Copyright © 2011, SAS Institute Inc. All rights reserved. #analytics2011
  52. 52. Post-Origination Neural NetworksExample Attrition Model 100% 90% 80% 70% 60% Sensitivity 50% 40% Random - Roc=50% 30% Logistic - Roc=65.92% 20% Sas Default MLP - Roc=68.09% 10% 0% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 1 - Specifity•Almost in all cases Neural Networks have a higher predictive power than Logistic Regression Copyright © 2011, SAS Institute Inc. All rights reserved. #analytics2011
  53. 53. Post-Origination Neural NetworksExample Attrition Model - Interpretability Continues variables Categorical variables Logistic Regression as a categorical variable Logistic Regression as a continues variable 1.2 1 1ρ 𝑥 = 1 + 𝑒− 𝐵0 +𝑥1 ∗𝐵1 …+𝑈_max⁡_12𝑀∗𝐵 𝑖 0.8 0.6 % Goods Beta 0.4 0.2 0 0 - 0.4 0.4 - 0.61 0.61- 1 𝑈_max⁡_12𝑀 Copyright © 2011, SAS Institute Inc. All rights reserved. #analytics2011
  54. 54. Post-Origination Neural Networks Example Attrition Model - Interpretability 1 2 Hidden Hidden Hidden Output 3 Layer Layer Layer Layer 4 1 2 3 5 1.1 2.1 3.1 6 Tan Tan Tan H H HInput Variables 7 8 9 1.2 2.2 3.2 10 Tan Tan Tan Out 11 H H H Put 12 13 14 1.3 2.3 3.3 Logistic Tan Tan Tan 15 H H H 16 17 Bias Bias 2 3 18 19 20 Bias There is no linear relationship between an input variable and the result 1 Copyright © 2011, SAS Institute Inc. All rights reserved. #analytics2011
  55. 55. Post-Origination Neural NetworksExample Attrition Model - Interpretability 0.85 Neural Network Variable Analysis 0.8 Score and Good Rate 0.75 0.7 0.65 0.6 U_max_12M Copyright © 2011, SAS Institute Inc. All rights reserved. #analytics2011
  56. 56. Post-Origination Neural NetworksExample Attrition Model - Interpretability Neural Network Variable Analysis 0.95 0.9 0.85 0.8 Score and Good Rate 0.75 0.7 0.65 0.6 0.55 0.5 0.45 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 MoB Copyright © 2011, SAS Institute Inc. All rights reserved. #analytics2011
  57. 57. Post-Origination Neural NetworksExample Attrition Model – Architecture Selection  To many architecture possibilities  Number of Hidden Layers and Units  Bias Unit  Activation Functions  Direct Connection Objetctive  Find the architecture with the best predictive power  Optimization  Genetic Algoritms Copyright © 2011, SAS Institute Inc. All rights reserved. #analytics2011
  58. 58. Post-Origination Neural NetworksExample Attrition Model – Architecture Selection Genetic Algorithm Optimization Optimization technique that attempts to replicate natural evolution processes Copyright © 2011, SAS Institute Inc. All rights reserved. #analytics2011
  59. 59. Post-Origination Neural NetworksExample Attrition Model – Architecture Selection Define objective function, input variables Generate initial population Decode chromosomes Evaluate each chromosome in the objective function Select parents Mating Mutation Convergence check Stop Copyright © 2011, SAS Institute Inc. All rights reserved. #analytics2011
  60. 60. Post-Origination Neural NetworksExample Attrition Model – Architecture Selection 100% 90% 80% 70% 60% Sensitivity 50% 40% Random - Roc=50% 30% Logistic - Roc=65.92% 20% Sas Default MLP - Roc=68.09% 10% GA - MLP 30 iters - Roc=71.25% 0% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 1 - Specifity Copyright © 2011, SAS Institute Inc. All rights reserved. #analytics2011
  61. 61. Post-Origination How to increased Models Predictive Power?  New Variables  Slope  R2  New Models  Neural Networks  Ensemble Models Copyright © 2011, SAS Institute Inc. All rights reserved. #analytics2011
  62. 62. Post-Origination Ensemble ModelWhy it works? Some unknown distribution Model 1 Model 6 Model 3 Model 5 Model 2 Model 4 Ensemble gives the global picture! Copyright © 2011, SAS Institute Inc. All rights reserved. #analytics2011
  63. 63. Post-Origination Ensemble ModelHow it works? Model 1 Combine multiple models  Majority voting  Average Model 2 Ensemble Model  Regression  Optimization  And others. Model N Copyright © 2011, SAS Institute Inc. All rights reserved. #analytics2011
  64. 64. Post-Origination Ensemble ModelAttrition Model Example 100% 90% 80% 70% 60% Sensitivity 50% 40% Random - Roc=50% Logistic - Roc=65.92% 30% Sas Default MLP - Roc=68.09% 20% GA - MLP 30 iters - Roc=71.25% 10% Ensemble - Roc=72.11% 0% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 1 - Specifity Copyright © 2011, SAS Institute Inc. All rights reserved. #analytics2011
  65. 65. Contact Information Alejandro Correa Andrés González Banco Colpatria Banco Colpatria Bogotá, Colombia Bogotá, Colombia al.bahnsen@gmail.com andrezfg@gmail.com Copyright © 2011, SAS Institute Inc. All rights reserved. #analytics2011

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