2011 advanced analytics through the credit cycle

Advanced Analytics through
the credit cycle
Alejandro Correa B.
Andrés Gonzalez M.

Copyright © 2011, SAS Institute Inc. All rights reserved. #analytics2011

Introduction

PRE-
ORIGINATION

Credit
Cycle
POST-
ORIGINATION
ORIGINATION


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


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.



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.


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

Pre-Origination High
Propensity ModelsMultiple offer
Propensity
to accept

Single offer

Tailor
made
products

Low
Propensity
to accept


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


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



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.


Cluster analysis


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


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


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


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


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


Origination Advance Strategies

Flow
Product
Selection
Initial Portfolio
offer
Association
Rules

Diferential
Scorecard

Predictive
Clusters


Predictive Cluster

3.3
3.7

6.5

8.9


Predictive Cluster


Diferential Scorecards
PROFILE 1 SCORE 1

CLASSIFICATION PROFILE 2 SCORE 2
MODEL

PROFILE 3 SCORE 3


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


Association Rules
 Product grade. 4 Empty Nest
Investment, travel

3 Growth of children
college and Retirement.

2 Newlywed

1 Young


Association Rules
 Product grade. 4 Empty Nest
Mortgage
Investment, travel

3 Growth of children
Vehicule
college and Retirement.

2 Newlywed
P-loan

1 Young
Credit Card


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%


Portfolio Offer

Association
Rules
Diferential
Risk Models

Classification
Model

Portfolio Offer


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


Origination
Portfolio Selection


Portfolio Selection

Product A

Client declined Product C
Product B

Product C


Portfolio Selection

Product A

Client want more credit
limit on Product A Product B


Post-Origination Maintenance
Traditional behavior strategies

Policies
Behavior
Score
 What about Profitability?
Current  Attrition?
Products

Offers


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


Profitability Model

Historic 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


Attrition Model
Historic 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


Solution

 Develop an index that combine clients Behavior,
Profitability and Attrition Scores

 CLIDI (Client Limit Increase Decrease Index)


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


New behavior strategy

Profitability
Score + Attrition
Score + Risk
Score = CLIDI

 The CLIDI Index is the weighted average of the 3 scores.


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


Post-Origination CLIDI distribution
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)


Post-Origination
How to increase 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, 3, 6, …, 24 months
Average, Response

New behavior variables
 Slope and linear regression R2.


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


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


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 Hidden Target
Layer Layer Layer
X1

X2

X3 score
X4

Bias 1 1


Post-Origination Neural Networks

|


Why Neural Networks?

Pros Cons
Interpretability
Predictive Power
Architecture
Selection


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


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


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


0.85
Neural Network Variable Analysis

0.8
Score and Good Rate

0.75

0.7

0.65

0.6

U_max_12M


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


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



 Genetic Algorithm Optimization
 Optimization technique that attempts to replicate natural evolution
processes



Define objective function, input variables

Generate initial population

Decode chromosomes

Evaluate each chromosome in the objective function

Select parents

Mating

Mutation

Convergence check

Stop


100%

90%

80%

70%

60%
Sensitivity

50%

40% Random - Roc=50%

30% Logistic - Roc=65.92%

20%

10%
GA - MLP 30 iters - Roc=71.25%

0%
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
1 - Specifity


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!


How it works?

Model 1

Combine multiple models
 Majority voting
 Average
Model 2
Ensemble
Model  Regression
 Optimization
 And others.

Model N


Attrition Model Example
100%

90%

80%

70%

60%
Sensitivity

50%

40% Random - Roc=50%

Logistic - Roc=65.92%
30%
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


Contact Information

Alejandro Correa Andrés González
Banco Colpatria Banco Colpatria
Bogotá, Colombia Bogotá, Colombia

al.bahnsen@gmail.com andrezfg@gmail.com


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