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2011 advanced analytics through the credit cycle
- 1. Advanced Analytics through
the credit cycle
Alejandro Correa B.
Andrés Gonzalez M.
Copyright © 2011, SAS Institute Inc. All rights reserved. #analytics2011
- 2. Introduction
PRE-
ORIGINATION
Credit
Cycle
POST-
ORIGINATION
ORIGINATION
Copyright © 2011, SAS Institute Inc. All rights reserved. #analytics2011
- 3. Introduction
Up sell Cross sell
Credit limit
Credit limit
Behavior
Portfolios Fraud
Fraud Free fall
Churn
Income
Origination Recovery
Identification
Collection
Propensity
Pre-Origination Origination Maintenance Collection
Copyright © 2011, SAS Institute Inc. All rights reserved. #analytics2011
- 5. 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.
Copyright © 2011, SAS Institute Inc. All rights reserved. #analytics2011
- 6. 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.
Copyright © 2011, SAS Institute Inc. All rights reserved. #analytics2011
- 7. Pre-Origination Propensity Models
Variables
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. 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. Pre-Origination Profile Analysis
Propensity 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. Pre-Origination Profile Analysis
Propensity 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. 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.
Copyright © 2011, SAS Institute Inc. All rights reserved. #analytics2011
- 12. Pre-Origination Profile Analysis
Cluster analysis
Copyright © 2011, SAS Institute Inc. All rights reserved. #analytics2011
- 13. Pre-Origination Results
High/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. Pre-Origination Results
High/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. Pre-Origination Results
Copyright © 2011, SAS Institute Inc. All rights reserved. #analytics2011
- 16. Pre-Origination Results
PROFILE 1 PROFILE 2 PROFILE 3
Response Response Response
Accept Don´t Accept Don´t Accept
Gender Gender Gender
Female Female Male
Age Age Age
56 Years or more 22 to 45 Years 36 Years or more
Up to date Active Obligations Up to date Active Obligations Up to date Active Obligations
2 or less 3 to 7 More than 5
Number or Mortgage Credits Number or Mortgage Credits Number or Mortgage Credits
None None 1 or more
Number 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$4.000
Average 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$1.500
Currently Active Checking Accounts Currently Active Checking Accounts Currently Active Checking Accounts
None None 1 or more
Currently Active Saving Accounts Currently Active Saving Accounts Currently Active Saving Accounts
None 1 2 or more
Offered 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. Pre-Origination Results
PROFILE 1 PROFILE 2 PROFILE 3
Response Response Response
Accept Don´t Accept Don´t Accept
Gender Gender Gender
Female Female Male
Age Age Age
56 Years or more Acceptance
22 to 45 Years Rate 36 Years or more
Up 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 Score
Number or Mortgage Credits Low Medium
Number or Mortgage Credits High
Number or Mortgage Credits
None None 1 or more
Number 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.
More
Average 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
More
Average 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$1.500
Currently Active Checking Accounts Currently Active Checking Accounts Currently Active Checking Accounts
None None 1 or more
Currently Active Saving Accounts Currently Active Saving Accounts Currently Active Saving Accounts
None 1 2 or more
Offered 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. Pre-Origination Results
Low 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
- 20. Origination Advance Strategies
Flow
Product
Selection
Initial Portfolio
offer
Association
Rules
Diferential
Scorecard
Predictive
Clusters
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- 21. Origination Advance Strategies
Predictive Cluster
3.3
3.7
6.5
8.9
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- 22. Origination Advance Strategies
Predictive Cluster
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- 23. Origination Advance Strategies
Diferential Scorecards
PROFILE 1 SCORE 1
CLASSIFICATION PROFILE 2 SCORE 2
MODEL
PROFILE 3 SCORE 3
Copyright © 2011, SAS Institute Inc. All rights reserved. #analytics2011
- 24. 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.
Empty Nest
Product grade. Investment, travel
Growth of children
Support (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. 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. 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. 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. 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. Origination Advance Strategies
Association 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. Origination Advance Strategies
Portfolio Offer
Association
Rules
Diferential
Risk Models
Classification
Model
Portfolio Offer
Copyright © 2011, SAS Institute Inc. All rights reserved. #analytics2011
- 29. Origination Advance Strategies
Initial 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
- 31. Origination Advance Strategies
Portfolio Selection
Product A
Client declined Product C
Product B
Product C
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- 32. Origination Advance Strategies
Portfolio Selection
Product A
Client want more credit
limit on Product A Product B
Copyright © 2011, SAS Institute Inc. All rights reserved. #analytics2011
- 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. 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. 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. 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. 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. Post-Origination Maintenance
High Profitability Score
CLIDI vs
Profitability Score High Attrition Score
High 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. 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. 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. 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. 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. 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. 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. 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
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- 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!
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- 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. 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. Post-Origination Neural Networks
|
Copyright © 2011, SAS Institute Inc. All rights reserved. #analytics2011
- 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. Post-Origination Neural Networks
Example 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
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- 53. 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
1 1
ρ 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
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- 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 H
Input 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
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- 55. Post-Origination Neural Networks
Example 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
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- 56. Post-Origination Neural Networks
Example 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
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- 57. 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
Objetctive
Find the architecture with the best predictive power
Optimization
Genetic Algoritms
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- 58. Post-Origination Neural Networks
Example Attrition Model – Architecture Selection
Genetic Algorithm Optimization
Optimization technique that attempts to replicate natural evolution
processes
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- 59. Post-Origination Neural Networks
Example 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
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- 60. Post-Origination Neural Networks
Example 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. 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. Post-Origination Ensemble Model
Why it works?
Some unknown distribution
Model 1 Model 6
Model 3 Model 5
Model 2 Model 4
Ensemble gives the global picture!
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- 63. Post-Origination Ensemble Model
How it works?
Model 1
Combine multiple models
Majority voting
Average
Model 2
Ensemble
Model Regression
Optimization
And others.
Model N
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- 64. Post-Origination Ensemble Model
Attrition 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
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- 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