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Loan Default Model

Loan Default Model. Saed Sayad. Data Mining Steps. 1. Problem Definition. Build loan default prediction model for small business using the historical data to assess the likelihood of default by an obligor. Data Mining Team. Domain Expert. 2. Data Preparation. No of Cases: 35,500

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Loan Default Model

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

  2. Data Mining Steps www.ismartsoft.com

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

  4. Data Mining Team Domain Expert www.ismartsoft.com

  5. 2. Data Preparation • 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%) www.ismartsoft.com

  6. 3. Data Exploration www.ismartsoft.com

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

  8. Data Exploration - Bivariate Months in Business and Default Default% www.ismartsoft.com

  9. 4. Modeling www.ismartsoft.com

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

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

  12. 5. Evaluation www.ismartsoft.com

  13. Evaluation – Variables Contribution www.ismartsoft.com

  14. Evaluation - Confusion Matrix Positive Cases Negative Cases Predicted Positive Predicted Negative www.ismartsoft.com

  15. Evaluation – Gain Chart Default% 100% 58% 10% Population% 10% 50% 100% www.ismartsoft.com

  16. 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 = $7,655,772 600% ROI www.ismartsoft.com

  17. 6. Deployment www.ismartsoft.com

  18. Questions? www.ismartsoft.com

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