2. Loan Default Model Saed Sayad 1www.ismartsoft.com

3. Data Mining Steps 1 Problem Definition 2 Data Preparation 3 Data Exploration 4 Modeling 5 Evaluation 6 Deployment www.ismartsoft.com2

4. 1. Problem Definition www.ismartsoft.com3 Build loan default prediction model for small business using the historical data to assess the likelihood of default by an obligor. Build loan default prediction model for small business using the historical data to assess the likelihood of default by an obligor.

5. Data Mining Team Modeler AnalystDBA www.ismartsoft.com4 Domain Expert

6. 2. Data Preparation www.ismartsoft.com5 No of Cases: 35,500 No of Defaults: 2,500 (7%) Number of Variables: 25 Total balance for all cases: $554,000,000 Total balance for defaults: $58,000,000 (10.4%) No of Cases: 35,500 No of Defaults: 2,500 (7%) Number of Variables: 25 Total balance for all cases: $554,000,000 Total balance for defaults: $58,000,000 (10.4%)

7. 3. Data Exploration Data Exploration Univariate Analysis Frequency, Average, Min, Max,... Bar, Line, Pie,... Charts Bivariate Analysis Correlation Z test,... Combination Charts www.ismartsoft.com6

8. Data Exploration - Univariate 7www.ismartsoft.com Months in Business

9. Data Exploration - Bivariate www.ismartsoft.com8 Default% Months in Business and Default

10. 4. Modeling Classification Bayesian Decision Tree Logistic Regression SVM Regression Linear Regression Robust Regression Neural Network Clustering HierarchicalK-Means Association A Priori www.ismartsoft.com9

11. Modeling - Classification www.ismartsoft.com10 f DELQ Age Type Default Y or N Logistic Regression

12. Logistic Regression Model 0 1 Linear Model Logistic Model Default Months in Business 11www.ismartsoft.com

13. 5. Evaluation ChartsStats Variables Contribution Mean Square Error Confusion Matrix K-S ChartLift ChartGain Chart www.ismartsoft.com12

14. Evaluation – Variables Contribution www.ismartsoft.com13

15. Evaluation - Confusion Matrix www.ismartsoft.com14 247 3% 264 3% 313 4% 7343 90% 8167 Positive Cases Negative Cases Predicted Positive Predicted Negative

16. Evaluation – Gain Chart www.ismartsoft.com15 Population% 50%10% 100% 58% 10% Default%

17. Return On Investment Total Number of Loans = 8,167 Total Number of Defaults = 560 Total Balance for Defaults = $12,281,589 Top 10% Random – Number of Defaults = 56 – Total Balance = $1,230,000 Top 10% Model – Number of Defaults = 305 – Total Balance = $7,655,772 Total Number of Loans = 8,167 Total Number of Defaults = 560 Total Balance for Defaults = $12,281,589 Top 10% Random – Number of Defaults = 56 – Total Balance = $1,230,000 Top 10% Model – Number of Defaults = 305 – Total Balance = $7,655,772 www.ismartsoft.com16 600% ROI

18. 6. Deployment www.ismartsoft.com17 SQL Batch Scoring HTML Web- based Scoring

19. www.ismartsoft.com18 Questions?