Credit scoring model

The First NIDA Business Analytics and Data Sciences Contest/Conference วันที่ 1-2 กันยายน 2559 ณ อาคารนวมินทราธิราช สถาบันบัณฑิตพัฒนบริหารศาสตร์

https://businessanalyticsnida.wordpress.com https://www.facebook.com/BusinessAnalyticsNIDA/

-การพัฒนาแบบจ าลองความเสี่ยงด้านเครดิต (Credit Scoring) ส าหรับการพิจารณาอนุมัติสินเชื่อ เพ่ือจัดการความเสี่ยงในการให้บริการสินเชื่อ -A-Score, B-Score, C-Score ?

สมสวัสดิ์ เตชพลฤทธินันท์ วทม. (NIDA)

นวมินทราธิราช 3002 วันที่ 1 กันยายน 2559 16.00-17.00 น.

Credit Scoring Model Somsawat T.

• Consider behavior / credit of customers (based on his/her knowledge or experience)

What is a credit scoring ? When you apply for credit, lenders want to make sure you can comfortably afford to manage any new borrowing. To do so, they usually calculate a credit score, weighing up all the relevant information at their disposal - this helps them to assess the chances that you will be able to repay what you owe.

People with a high score are usually seen as lower risk, and could therefore be more likely to be granted credit - and possibly at better rates.

--Experian--

Type of Credit Scoring

Application Score

Behavior Score

Collection Score

• Expert judgment • Create without historical data • Suitable for new segment/product

The ways to develop credit scoring

Judgmental

• Historical base • Statistics technic eg. Decision tree , Logistic

regression , Neural network • Suitable for product has sufficient data

Statistical

• Combination of expert judgment and statistical • Suitable for new product/segment which have

similar feature with existing product

Hybrid

• Objective

• Performance window • Observation / Sample window

• Bad definition

Good/Bad?

Time

Sample Window

Observation Window

Performance Window

Sample Window

Jan’12 Dec’12 Jun’13

Out of Time Validation

Sample

Development Sample (100%)

Training Sample (70%)

Testing (Hold-out sample)

(30%)

Income Total Good Bad %Dist_Total %Dist_Good %Dist_Bad Bad rate WOE IV

<15,000 20,057 19,418 639 38.77% 38.30% 62.28% 3.19% -0.49

15,001-20,000 3,351 3,250 101 6.48% 6.41% 9.84% 3.01% -0.43

20,001-30,000 10,031 9,916 115 19.39% 19.56% 11.21% 1.15% 0.56

>30,000 18,293 18,122 171 35.36% 35.74% 16.67% 0.93% 0.76

Total 51,732 50,706 1,026 100.00% 100.00% 100.00% 1.98%

Age Total Good Bad %Dist_Total %Dist_Good %Dist_Bad Bad rate WOE IV

Low <-= 28 8,377 8,081 296 16.19% 15.94% 28.85% 3.53% -0.59

28 <-= 33 10,293 10,023 270 19.90% 19.77% 26.32% 2.62% -0.29

33 <-= 40 12,318 12,101 217 23.81% 23.87% 21.15% 1.76% 0.12

40 <-= High 20,744 20,501 243 40.10% 40.43% 23.68% 1.17% 0.53

Total 51,732 50,706 1,026 100.00% 100.00% 100.00% 1.98%

0.323

0.188

i

ii

BadDistr

GoodDistrWOE

_

_ln

BadDistr

GoodDistrBadDistrGoodDistrIV

n

i

ii_

_ln*)__(

1

Choosing a Scorecard

• Model performance

• Model Stability

Score cut-off and Strategy (Illustrative : A-score)

594-654 655-669 670-675 676-682 683-687 686-692 693-697 698-702 703-707 708-714 715-719

683-687

686-692

693-697

698-702

703-707

708-714

715-719

720-720

721-727

728-731

A1 Score

A2

-Sco

re

Important Skills

Thank you Somsawat T.