2011 advanced analytics through the credit cycle
DESCRIPTION
Presentation at the SAS Analytics Conference 2011, Orlando, FL. Presenters: Alejandro Correa Bahnsen Andres Felipe Gonzalez MontoyaTRANSCRIPT
Copyright © 2011, SAS Institute Inc. All rights reserved. #analytics2011
Advanced Analytics through the credit cycle Alejandro Correa B. Andrés Gonzalez M.
Copyright © 2011, SAS Institute Inc. All rights reserved. #analytics2011
Credit Cycle
PRE-ORIGINATION
ORIGINATION POST-
ORIGINATION
Introduction
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Introduction
Pre-Origination Origination Maintenance Collection
Identification
Propensity
Origination
Credit limit
Fraud
Behavior
Up sell Cross sell
Fraud
Churn
Recovery
Collection
Free fall Portfolios
Income
Credit limit
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Copyright © 2011, SAS Institute Inc. All rights reserved. #analytics2011
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.
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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.
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Pre-Origination Propensity Models
Variables
Bureau: Credit behavior information.
Demographic: Personal information.
Age
Gender Buerau Inquiries
Marital Status
Education
Credit Experience
Delinquencies
Current Products
Quantity of C.C.
City
Credit Limit
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High
Propensity
to accept
Low
Propensity
to accept
Tailor
made
products
Pre-Origination Propensity Models
Single offer
Multiple offer
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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
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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
Tailor made
products
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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.
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Pre-Origination Profile Analysis
Cluster analysis
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Pre-Origination Results
High/Medium Propensity (Product Acceptance)
17.000%
18.000%
19.000%
20.000%
21.000%
22.000%
23.000%
24.000%
23.110%
19.580%
With propensity model Without propensity model
Increase: 18%
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Pre-Origination Results
High/Medium Propensity (Product Acceptance)
17.000%
18.000%
19.000%
20.000%
21.000%
22.000%
23.000%
24.000%
23.110%
19.580%
With propensity model Without propensity model
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%
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Pre-Origination Results
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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
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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
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%
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Pre-Origination Results
Low Propensity (Product Acceptance)
.000%
2.000%
4.000%
6.000%
8.000%
10.000%
12.000%
14.000%
16.000%
18.000%
20.000%
Profile 1 Profile 2 Profile 3
7.680%
17.060%
18.940%
5.130%
9.630%
6.250%
Tailor made product Regular product
Increase: 200%
Increase: 77%
Increase: 50%
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Origination Advance Strategies
Flow
Predictive Clusters
Diferential Scorecard
Association Rules
Initial Portfolio offer
Product Selection
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Origination Advance Strategies
Predictive Cluster
3.7
3.3
8.9
6.5
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Origination Advance Strategies
Predictive Cluster
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Origination Advance Strategies
Diferential Scorecards
PROFILE 1
PROFILE 2
PROFILE 3
CLASSIFICATION MODEL
SCORE 1
SCORE 2
SCORE 3
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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
Young Savings for future purchases
Buy home and meet family needs
Growth of children
Empty Nest Investment, travel
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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
1
2 3
4
Young Savings for future purchases
Newlywed Buy home and meet family needs
Growth of children college and Retirement.
Empty Nest Investment, travel
Copyright © 2011, SAS Institute Inc. All rights reserved. #analytics2011
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
1
2 3
4
Young Savings for future purchases
Newlywed Buy home and meet family needs
Growth of children college and Retirement.
Empty Nest Investment, travel Mortgage
Vehicule
P-loan
Credit Card
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Origination Advance Strategies
Association Rules Results
C.C. C.C. Support: 28.56%
C.C. P-loan Support: 16.22%
C.C. C.C. P-loan Support: 12.61%
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Origination Advance Strategies
Portfolio Offer
Portfolio Offer
Classification Model
Diferential Risk Models
Association Rules
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Origination Advance Strategies
Initial Portfolio Offer
Client Income
Debt
Montly Installment Calculated using
client risk and profile
Product A
Product B
Product C
Monthly Installment is
divided in number of
products according to
Association Rules
Model
Remaining
Income
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Origination
Portfolio Selection
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Origination Advance Strategies
Product C
Product A
Product B
Portfolio Selection
Client declined Product C
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Origination Advance Strategies
Product A
Product B
Portfolio Selection
Client want more credit
limit on Product A
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Post-Origination Maintenance
Traditional behavior strategies
Offers
Current Products
Behavior Score
Policies
What about Profitability?
Attrition?
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Behavior Model
Historic Variables
+
Demographic Variables
+
Bureau Variables
Observation
Point
Days Past Due
Behavior Month1 Month 2 Month T
Forecast client loan behavior using its past behavior
Y = maximum dpd over performance window
Post-Origination Maintenance
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Profitability Model
Forecast client profitability using its past behavior
Differences Between Models
A good behavior score does not necessary mean a good profitability
Y = Cumulative profitability over performance window
Post-Origination Maintenance
Historic Variables
+
Demographic Variables
+
Bureau Variables
Observation
Point
Profitability
Behavior Month1 Month 2 Month T
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Attrition Model
Historic Variables
+
Demographic Variables
+
Bureau Variables
Observation
Point
Attrition Month1
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
Y = Clients Attrition over the performance window
Post-Origination Maintenance
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Solution
Develop an index that combine clients Behavior, Profitability and Attrition Scores
CLIDI (Client Limit Increase Decrease Index)
Post-Origination Maintenance
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Attrition Score
Behavior Score
High Behavior Score
vs
High Attrition Score
High Profitability Score
vs
High Attrition Score
High Profitability Score
vs
High Behavior Score
Profitability Score CLIDI
Post-Origination Maintenance
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New behavior strategy
The CLIDI Index is the weighted average of the 3 scores.
Profitability Score
Attrition Score
Risk Score CLIDI + + =
Post-Origination Maintenance
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New behavior strategy
Offers
Current Products
CLIDI Policies
Clients that receive the offer are the best in terms of behavior score and profitability score
Also strategies are develop to decreased good clients attrition
Profitability Score
Attrition Score
Credit card
Behavior Model
Post-Origination Maintenance
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Post-Origination CLIDI distribution
Profitability 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
New behavior strategy B
eha
vio
r S
core
Average CLIDI Agresive
Strategies
Taylor made
Strategies
(Control Groups)
No Strategy
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How to increase Models Predictive Power?
New Variables
Slope
R2
New Models
Neural Networks
Ensemble Models
Post-Origination
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Post-Origination Variables
Traditional behavior variables
Variable Calculation Time
Purchases Sum, Max, Average, Count 3, 6, …, 24 months
DPD Count, Max, Min, Average, Standard
Deviation 3, 6, …, 24 months
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.
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Example
Traditional variables are the same for both clients
.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
90.00%
100.00%1
001
10
02
10
03
10
04
10
05
10
06
10
07
10
08
10
09
10
10
10
11
10
12
Utu
liza
tio
n
Month
Client 1
Client 2
Statistic Client 1 Client 2
Average 56% 56%
Std 22% 22%
Min 19% 20%
Max 91% 91%
Slope 11% -10%
Post-Origination Variables
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Example
Traditional variables are the same for both clients
Statistic Client 1 Client 2
Average 37% 35%
Std 23% 23%
Min 4% 4%
Max 75% 79%
Slope -17% -16%
R2 99% 76% .000%
10.000%
20.000%
30.000%
40.000%
50.000%
60.000%
70.000%
80.000%
90.000%
1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012
Uti
liza
tio
n
Month
Client 1
Client 2
Post-Origination Variables
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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 Variables
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How to increased Models Predictive Power?
New Variables
Slope
R2
New Models
Neural Networks
Ensemble Models
Post-Origination
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Post-Origination
Mathematical model that tries to imitate a biological neuron.
Consist in tree parts: Input Layer; Hidden Layer; Target Layer.
X1
X2
X4
X3
1
Input
Layer
1
Hidden
Layer
Target
Layer
Bias
score
Neural Networks
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Post-Origination Neural Networks
|
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Post-Origination Neural Networks
Pros
Predictive Power
Cons
Interpretability
Architecture Selection
Why Neural Networks?
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Post-Origination Neural Networks
•Almost in all cases Neural Networks have a higher predictive power than Logistic Regression
Example Attrition Model
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Se
ns
itiv
ity
1 - Specifity
Random - Roc=50%
Logistic - Roc=65.92%
Sas Default MLP - Roc=68.09%
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Post-Origination Neural Networks
Logistic Regression as a continues variable
ρ 𝑥 =1
1 + 𝑒− 𝐵0+𝑥1∗𝐵1…+𝑈_max_12𝑀∗𝐵𝑖
Logistic Regression as a categorical variable
0
0.2
0.4
0.6
0.8
1
1.2
0 - 0.4 0.4 - 0.61 0.61- 1
% Goods
Beta
𝑈_max_12𝑀
Example Attrition Model - Interpretability
Continues variables Categorical variables
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Post-Origination Neural Networks
There is no linear relationship between an input variable and the result
Hidden
Layer
3
Hidden
Layer
1
Hidden
Layer
2
Output
Layer
16
17
18
19
20
11
12
13
14
15
6
7
8
9
10
1
2
3
4
5
1.3
Tan
H
1.1
Tan
H
1.2
Tan
H
2.1
Tan
H
2.2
Tan
H
2.3
Tan
H
3.1
Tan
H
3.2
Tan
H
3.3
Tan
H
Out
Put
Bias
2
Bias
3
Bias
1
Logistic
Input
Vari
able
s
Example Attrition Model - Interpretability
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Post-Origination Neural Networks
Neural Network Variable Analysis
Example Attrition Model - Interpretability
0.6
0.65
0.7
0.75
0.8
0.85
Sc
ore
an
d G
oo
d R
ate
U_max_12M
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Post-Origination Neural Networks
Neural Network Variable Analysis
Example Attrition Model - Interpretability
0.45
0.5
0.55
0.6
0.65
0.7
0.75
0.8
0.85
0.9
0.95
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
Sc
ore
an
d G
oo
d R
ate
MoB
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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
Find the architecture with the best predictive power
Optimization
Genetic Algoritms
Objetctive
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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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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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Post-Origination Neural Networks
Example Attrition Model – Architecture Selection
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Se
ns
itiv
ity
1 - Specifity
Random - Roc=50%
Logistic - Roc=65.92%
Sas Default MLP - Roc=68.09%
GA - MLP 30 iters - Roc=71.25%
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Post-Origination
How to increased Models Predictive Power?
New Variables
Slope
R2
New Models
Neural Networks
Ensemble Models
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Post-Origination Ensemble Model
Why it works?
Ensemble gives the global picture!
Model 1
Model 2
Model 3
Model 4
Model 5 Model 6
Some unknown distribution
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Post-Origination Ensemble Model
How it works?
Model 1
Model 2
Model N
Ensemble Model
Combine multiple models
Majority voting
Average
Regression
Optimization
And others.
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Post-Origination Ensemble Model
Attrition Model Example
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Se
ns
itiv
ity
1 - Specifity
Random - Roc=50%
Logistic - Roc=65.92%
Sas Default MLP - Roc=68.09%
GA - MLP 30 iters - Roc=71.25%
Ensemble - Roc=72.11%
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Contact Information
Alejandro Correa
Banco Colpatria
Bogotá, Colombia
Andrés González
Banco Colpatria
Bogotá, Colombia