10. Minimal increase in average interest rate from 6% to 6.8%
Would have minimized losses in 2014 from ~$19K to ~$2K ($17K and 89% improvement)
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Density
11. Minimal increase in average interest rate from 6% to 6.8%
Would have minimized losses in 2014 from ~$19K to ~$2K ($17K and 89% improvement)
Would have minimized losses from 2009 onwards from ~$293K to ~$53K ( $240K and 82% improvement)
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Density
12. Predictive model created from combination of logistic regression and machine learning (SVM)
ā¢Basic probability theory to deal with class bias
13. Predictive model created from combination of logistic regression and machine learning (SVM)
ā¢Basic probability theory to deal with class bias
ķķķķ ķ =ķķķķķķ¢ķķ”ā(1āķķ ķķķķķķ¦ķķķķ”ķķķķķ¢ķķ”)
14. Predictive model created from combination of logistic regression and machine learning (SVM)
ā¢Basic probability theory to deal with class bias
ķķķķ ķ =ķķķķķķ¢ķķ”ā(1āķķ ķķķķķķ¦ķķķķ”ķķķķķ¢ķķ”)
15. Predictive model created from combination of logistic regression and machine learning (SVM)
Density
ā¢Basic probability theory to deal with class bias
ā¢Logistic regression identified 4 features that could predict risk
ā¢āRiskier populationā
ā¢Borrower allowed maximum interest rate
ā¢Loan Category
ā¢Country of applicant
16. Predictive model created from combination of logistic regression and machine learning (SVM)
Density
ā¢Basic probability theory to deal with class bias
ā¢Logistic regression identified 4 features that could predict risk
ā¢āRiskier populationā
ā¢Borrower allowed maximum interest rate
ā¢Loan Category
ā¢Country of applicant
17. Higher Risk Associated with Borrowers who entered between August 2012 and August 2013
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18. Higher Risk Associated with Borrowers who entered between August 2012 and August 2013
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August 2012
August 2013
19. Predictive model created from combination of logistic regression and machine learning (SVM)
ā¢Basic probability theory to deal with class bias
ā¢Logistic regression identified 4 features that could predict risk
ā¢āRiskier populationā
ā¢Borrower allowed maximum interest rate
ā¢Loan Category
ā¢Country of applicant
ā¢Used identified features to train
kernel SVM with 10 fold cross validation
(89% loss recovery)
20. ā¢Impact/Significance
ā¢Project to recover $48,000 over the next year from loss
ā¢Over 5 year period, for every $1 million invested, recovers additional $110,000 that can continue to be reinvested
ā¢Actions already taken
ā¢Implement the model the risk model for interest rates
ā¢Change policy to ask for borrower allowed interest rates again
ā¢Actions to be taken
ā¢Find policy change that allowed for risky population
Conclusions