Advanced Analytics through the credit cycle Alejandro Correa B. Andrés Gonzalez M.
Introduction PRE ORIGINATION Credit Cycle POST ORIGINATION ORIGINATION
Pre-Origination Propensity Models What 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.
Pre-Origination Propensity Models Objectives 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.
Pre-Origination Propensity Models Variables Bureau: Credit behavior information. Demographic: Personal information. Credit Experience Gender Buerau Inquiries Delinquencies Stratum Current Products Marital Status Credit Limit Education Age City Quantity of C.C.
Pre-Origination Propensity Models High Propensity to accept Multiple offer Single offer Tailor made products Low Propensity to accept
Pre-Origination Profile Analysis Cluster 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.
Pre-Origination Profile Analysis Cluster analysis
Pre-Origination Profile Analysis Propensity vs Risk Acceptance Rate Propensity Score Bureau 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
Pre-Origination Results High Propensity (Product Acceptance) 24.000% 23.110% 23.000% 22.000% 21.000% 19.580% 20.000% 19.000% 18.000% 17.000% With propensity model Without propensity model
Pre-Origination Results PROFILE 1 Response Accept Gender Female Age 56 Years or more Up to date Active Obligations 2 or less Number or Mortgage Credits None Number of total Credit Cards 0 or 1 C.C. Average Credit Card Limits 0 Average Credit Card Utilization 0% Approved Credit limit in Colpatria Less than US$450 Currently Active Checking Accounts None Currently Active Saving Accounts None Offered Credit Card Visa Clasic Mastercard Clasic PROFILE 2 Response Don t Accept Gender Female Age 22 to 45 Years Up to date Active Obligations 3 to 7 Number or Mortgage Credits None Number of Credit Card 2 or 3 C.C. Average Credit Card Limits Less than US$4.000 Average Credit Card Utilization More than 9% Approved Credit limit in Colpatria US$450 to US$1.500 Currently Active Checking Accounts None Currently Active Saving Accounts 1 Offered Credit Card Visa Clasic Mastercard Clasic PROFILE 3 Response Don t Accept Gender Male Age 36 Years or more Up to date Active Obligations More than 5 Number or Mortgage Credits 1 or more Number of Credit Cards More than 3 C.C. Average Credit Card Limits More than US$4.000 Average Credit Card Utilization 1% to 37% Approved Credit limit in Colpatria More than US$1.500 Currently Active Checking Accounts 1 or more Currently Active Saving Accounts 2 or more Offered Credit Card Visa Gold and Platinum Mastercard Gold and Platinum
Pre-Origination Results Low Propensity (Product Acceptance) 18.940% 20.000% 17.060% 18.000% 16.000% 14.000% 12.000% 10.000% 8.000% 7.680% 5.130% 9.630% 6.250% 6.000% 4.000% 2.000%.000% Profile 1 Profile 2 Profile 3 Tailor made product Regular product
Origination Advance Strategies Flow Association Rules Initial Portfolio offer Product Selection Diferential Scorecard Predictive Clusters
Origination Advance Strategies Predictive Cluster 3.7 3.3 6.5 8.9
Origination Advance Strategies Diferential Scorecards PROFILE 1 SCORE 1 CLASSIFICATION MODEL PROFILE 2 SCORE 2 PROFILE 3 SCORE 3
Origination Advance Strategies Association 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. Support (x,y): Number of times that appears the combination (x,y) / Total Transaction
Origination Advance Strategies Association Rules Results C.C. C.C. Revolving C.C. C.C. Revolving C.C. Installment Support: 18.56% Support: 6.22% Support: 2.71% Support: 1.88% C.C. C.C. Revolving C.C. Revolving. C.C. Support: 1.55% Support: 1.35%
Origination Advance Strategies Portfolio Offer Classification Model Risk Models Association Rules Portfolio Offer
Origination Advance Strategies Initial Portfolio Offer Remaining Income Product A Montly Client Installment Income Monthly Installment is divided Calculated in number usingof products client risk according and profile to Association Rules Model Product B Debt Product C
Origination Portfolio Selection
Origination Advance Strategies Portfolio Selection Product A Client declined Product C Product B Product C
Origination Advance Strategies Portfolio Selection Product A Client want more credit limit on Product A Product B
Post-Origination Maintenance Traditional behavior strategies Behavior Score Policies Current Products What about Profitability? Attrition? Offers
Post-Origination Maintenance Behavior Model Historic Variables + Demographic Variables + Bureau Variables Observation Point Month1 Month 2 Month T Days Past Due Behavior Y = maximum dpd over performance window Forecast client loan behavior using its past behavior
Post-Origination Maintenance Profitability Model Historic Variables + Demographic Variables + Bureau Variables Observation Point Month1 Month 2 Month T Profitability Behavior 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
Post-Origination Maintenance Attrition Model Historic Variables + Demographic Variables + Bureau Variables Observation Point Month1 Attrition 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
Post-Origination Maintenance Solution Develop an index that combine clients Behavior, Profitability and Attrition Scores CLIDI (Client Limit Increase Decrease Index)
Post-Origination Maintenance CLIDI High Profitability Score vs High Behavior Score Profitability Score High Profitability Score vs High Attrition Score Attrition Score Behavior Score High Behavior Score vs High Attrition Score
Post-Origination Maintenance New behavior strategy Profitability Score + Attrition + Risk = Score Score CLIDI The CLIDI Index is the weighted average of the 3 scores.
Post-Origination Maintenance New behavior strategy Profitability Score Attrition Score CLIDI Policies Clients that receive the offer are the best in terms of behavior score and profitability score Credit card Behavior Model Current Products Also strategies are develop to decreased good clients attrition Offers
Post-Origination CLIDI distribution New behavior strategy Average CLIDI Agresive Strategies Behavior Score 10 46 52 57 62 66 69 73 77 80 82 9 42 48 55 59 63 67 71 74 77 79 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 No Strategy Profitability Score Taylor made Strategies (Control Groups)
Post-Origination How to increased Models Predictive Power? New Variables Slope R2 New Models Neural Networks Ensemble Models
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, Average, Response 3, 6,, 24 months New behavior variables Slope and linear regression R2.
Post-Origination Variables Example 100.00% 90.00% Statistic Client 1 Client 2 80.00% Average 56% 56% 70.00% Utulization 60.00% 50.00% 40.00% Client 1 Client 2 Std 22% 22% Min 19% 20% 30.00% 20.00% Max 91% 91% 10.00%.00% Slope 11% -10% 1001 1002 1003 1004 1005 1006 Month 1007 1008 1009 1010 1011 1012 Traditional variables are the same for both clients
Post-Origination Variables Example 90.000% 80.000% 70.000% 60.000% Client 1 Client 2 Statistic Client 1 Client 2 Average 37% 35% Std 23% 23% Utilization 50.000% 40.000% 30.000% Min 4% 4% Max 75% 79% 20.000% Slope -17% -16% 10.000%.000% 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 Month R2 99% 76% Traditional variables are the same for both clients
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!
Post-Origination How to increased Models Predictive Power? New Variables Slope R2 New Models Neural Networks Ensemble Models
Post-Origination Neural Networks Mathematical model that tries to imitate a biological neuron. Consist in tree parts: Input Layer; Hidden Layer; Target Layer. Input Layer Hidden Layer Target Layer X1 X2 X3 score X4 Bias 1 1
Post-Origination Neural Networks
Post-Origination Neural Networks Why Neural Networks? Pros Predictive Power Cons Interpretability Architecture Selection
Post-Origination Neural Networks Example Attrition Model 100% 90% 80% 70% Sensitivity 60% 50% 40% 30% 20% 10% Random - Roc=50% Logistic - Roc=65.92% Sas Default MLP - Roc=68.09% 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
Post-Origination Neural Networks Example Attrition Model - Interpretability Continues variables Categorical variables Logistic Regression as a categorical variable Logistic Regression as a continues variable 1.2 ρ 1 1 _ _ 1 0.8 0.6 0.4 0.2 % Goods Beta 0 0-0.4 0.4-0.61 0.61-1 _max _12
Post-Origination Neural Networks Example Attrition Model - Interpretability 1 2 3 4 Hidden Layer 1 Hidden Layer 2 Hidden Layer 3 Output Layer Input Variables 5 6 7 8 9 10 11 12 1.1 Tan H 1.2 Tan H 2.1 Tan H 2.2 Tan H 3.1 Tan H 3.2 Tan H Out Put 13 14 15 1.3 Tan H 2.3 Tan H 3.3 Tan H Logistic 16 17 18 Bias 2 Bias 3 19 20 Bias 1 There is no linear relationship between an input variable and the result
Post-Origination Neural Networks Example Attrition Model - Interpretability Neural Network Variable Analysis
Post-Origination Neural Networks Example Attrition Model Architecture Selection To many architecture possibilities Number of Hidden Layers and Units Bias Unit Activation Functions Direct Connection Objective Find the architecture with the best predictive power Genetic Algorithm Optimization
Post-Origination Neural Networks Example Attrition Model Architecture Selection Define objective function, input variables Generate initial population Genetic Algorithm Optimization Decode chromosomes Optimization technique that attempts to replicate natural evolution Evaluate each chromosome in the objective function Variable 1 processes Select parents Mating 00 = 1 01 = 2 10 = 3 11 = 4 0 1 f x 85 73 42 n Variable n 1 0 = No 1 = Yes 39 Mutation Convergence check Elite Solution Stop
Post-Origination Neural Networks Example Attrition Model Architecture Selection 100% 90% 80% 70% Sensitivity 60% 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
Post-Origination How to increased Models Predictive Power? New Variables Slope R2 New Models Neural Networks Ensemble Models
Post-Origination Ensemble Model How it works? Model 1 Model 2 Ensemble Model Combine multiple models Majority voting Average Regression Optimization And others. Model N
Post-Origination Ensemble Model Why it works? Some unknown distribution Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Ensemble gives the global picture!
Post-Origination Ensemble Model Attrition Model Example 100% 90% 80% 70% Sensitivity 60% 50% 40% 30% 20% 10% Random - Roc=50% Logistic - Roc=65.92% Sas Default MLP - Roc=68.09% GA - MLP 30 iters - Roc=71.25% Ensemble - Roc=72.11% 0% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 1 - Specifity
Contact Information Alejandro Correa Banco Colpatria Bogotá, Colombia (+57) 3208306606 correaal@colpatria.com Andrés González Banco Colpatria Bogotá, Colombia (+57) 3103595239 gonzalean@colpatria.com