2011 advanced analytics through the credit cycle

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

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


Credit Cycle

PRE-ORIGINATION

ORIGINATION POST-

ORIGINATION

Introduction


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



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.

Age

Gender Buerau Inquiries

Marital Status

Education

Credit Experience

Delinquencies

Current Products

Quantity of C.C.

City

Credit Limit


High

Propensity

to accept

Low

Propensity

to accept

Tailor

made

products


Single offer

Multiple offer


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



Propensity vs Risk

Acceptance Rate


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



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)

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%



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


Low Medium High

Low 23.65% 31.05% 49.42%

Medium 63.75% 65.61% 75.47%

High 83.69% 85.80% 87.36%





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 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 Saving Accounts 1


Mastercard Clasic

PROFILE 3


Gender Male


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



PROFILE 1

Response Accept

Gender Female


Up to date Active Obligations 2 or less


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 Saving Accounts None


Mastercard Clasic

PROFILE 2


Gender Female

Age 22 to 45 Years

Up to date Active Obligations 3 to 7


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 Saving Accounts 1


Mastercard Clasic

PROFILE 3


Gender Male


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


Low Medium High

Low 23.65% 31.05% 49.42%

Medium 63.75% 65.61% 75.47%

High 83.69% 85.80% 87.36%



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%



Origination Advance Strategies

Flow

Predictive Clusters

Diferential Scorecard

Association Rules

Initial Portfolio offer

Product Selection



Predictive Cluster

3.7

3.3

8.9

6.5



Predictive Cluster



Diferential Scorecards

PROFILE 1

PROFILE 2

PROFILE 3

CLASSIFICATION MODEL

SCORE 1

SCORE 2

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.

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



Association Rules



Products acquired.


Costumer maturity.

Product grade.


1

2 3

4


Newlywed Buy home and meet family needs

Growth of children college and Retirement.

Empty Nest Investment, travel



Association Rules



Products acquired.


Costumer maturity.

Product grade.


1

2 3

4


Newlywed Buy home and meet family needs

Growth of children college and Retirement.

Empty Nest Investment, travel Mortgage

Vehicule

P-loan

Credit Card



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%



Portfolio Offer

Portfolio Offer

Classification Model

Diferential Risk Models

Association Rules



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


Origination

Portfolio Selection



Product C

Product A

Product B

Portfolio Selection

Client declined Product C



Product A

Product B

Portfolio Selection

Client want more credit

limit on Product A



Post-Origination Maintenance

Traditional behavior strategies

Offers

Current Products

Behavior Score

Policies

What about Profitability?

Attrition?


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



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


Historic Variables

+


+

Bureau Variables

Observation

Point

Profitability

Behavior Month1 Month 2 Month T


Attrition Model

Historic 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



Solution

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

CLIDI (Client Limit Increase Decrease Index)



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



New behavior strategy

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

Profitability Score

Attrition Score

Risk Score CLIDI + + =



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 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


How to increase Models Predictive Power?

New Variables

Slope

R2

New Models

Neural Networks

Ensemble Models

Post-Origination


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.


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%



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



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!



How to increased Models Predictive Power?

New Variables

Slope

R2

New Models

Neural Networks

Ensemble Models

Post-Origination


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


Post-Origination Neural Networks

|



Pros

Predictive Power

Cons

Interpretability

Architecture Selection

Why 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%



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



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




Neural Network Variable Analysis


0.6

0.65

0.7

0.75

0.8

0.85

Sc

ore

an

d G

oo

d R

ate

U_max_12M



Neural Network Variable Analysis


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



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




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




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%



GA - MLP 30 iters - Roc=71.25%


Post-Origination

How to increased Models Predictive Power?

New Variables

Slope

R2

New Models

Neural Networks

Ensemble Models


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



How it works?

Model 1

Model 2

Model N

Ensemble Model

Combine multiple models

Majority voting

Average

Regression

Optimization

And others.



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%



GA - MLP 30 iters - Roc=71.25%

Ensemble - Roc=72.11%


Contact Information

Alejandro Correa

Banco Colpatria

Bogotá, Colombia

[email protected]

Andrés González

Banco Colpatria

Bogotá, Colombia

[email protected]

mailto:[email protected]

mailto:[email protected]

Date post:	02-Jul-2015
Category:	Business
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2011 advanced analytics through the credit cycle

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