การพัฒนาแบบจำลองความเสี่ยงด้านเครดิต...
TRANSCRIPT
Credit scoring model
The First NIDA Business Analytics and Data Sciences Contest/Conference วันที่ 1-2 กันยายน 2559 ณ อาคารนวมินทราธิราช สถาบันบัณฑิตพัฒนบริหารศาสตร์
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-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%
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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.