This document discusses using machine learning models to help set sustainable interest rates for small businesses in Africa in order to minimize loan losses. The author created a predictive model combining logistic regression and support vector machines (SVM) that identified borrower characteristics predicting risk. While SVM recovered some losses, probability theory was also needed to achieve around 89% loss recovery. Implementing variable interest rates based on this model could have reduced losses by $240,000 over several years. The model's use going forward could continue recovering losses to reinvest in more loans.

1. Identifying sustainable interest rates while helping
African small businesses grow
Jack Chai
Insight Data Science Fellow
2014

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Loss Risk = Fraction of Money Not Paid Back

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Loss Risk = Fraction of Money Not Paid Back
Actual Trend in 2014

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Loss Risk = Fraction of Money Not Paid Back
Desired Trend
Actual Trend in 2014

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Minimal increase in average interest rate from 6% to 6.8%
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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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Minimal increase in average interest rate from 6% to 6.8%
Would have minimized losses in 2014 from ~$19K to ~$2K (89% improvement)
Would have minimized losses from 2009 onwards from ~$293K to ~$53K ( $240K and 82%
improvement)
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12. Predictive model created from combination of
logistic regression and machine learning (SVM)
• Logistic regression identified several features that could predict risk

13. Predictive model created from combination of
logistic regression and machine learning (SVM)
• Logistic regression identified several features that could predict risk
• “Riskier population”
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14. Predictive model created from combination of
logistic regression and machine learning (SVM)
• Logistic regression identified several features that could predict risk
• “Riskier population”
Density

15. Predictive model created from combination of
logistic regression and machine learning (SVM)
• Logistic regression identified several features that could predict risk
• “Riskier population”
Density
August 2012
August 2013

16. Predictive model created from combination of
logistic regression and machine learning (SVM)
• Logistic regression identified several features that could predict risk
• “Riskier population”
• Borrower allowed maximum interest rate
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17. Predictive model created from combination of
logistic regression and machine learning (SVM)
• Logistic regression identified several features that could predict risk
• “Riskier population”
• Borrower allowed maximum interest rate
• Training with SVM only got us part of the way (22% recovery)
• Had to go back to simple probability theory
Density

18. Predictive model created from combination of
logistic regression and machine learning (SVM)
• Logistic regression identified several features that could predict risk
• “Riskier population”
• Borrower allowed maximum interest rate
• Training with SVM only got us part of the way (22% recovery)
• Had to go back to simple probability theory
푃 푙표푠푠
= 푃 푑푒푓푎푢푙푡 ∗ (1 − 푃 푠표푚푒푝푎푦푚푒푛푡 푑푒푓푎푢푙푡 )
Density

19. Predictive model created from combination of
logistic regression and machine learning (SVM)
• Logistic regression identified several features that could predict risk
• “Riskier population”
• Borrower allowed maximum interest rate
• Training with SVM only got us part of the way (22% recovery)
• Had to go back to simple probability theory
푃 푙표푠푠
= 푃 푑푒푓푎푢푙푡 ∗ (1 − 푃 푠표푚푒푝푎푦푚푒푛푡 푑푒푓푎푢푙푡 )
Density

20. Predictive model created from combination of
logistic regression and machine learning (SVM)
• Logistic regression identified several features that could predict risk
• “Riskier population”
• Borrower allowed maximum interest rate
• Training with SVM only got us part of the way (22% recovery)
• Had to go back to simple probability theory
• Combined retrained SVM with probability theory to achieve ~89%
loss recovery
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21. Conclusions
• Impact/Significance
• Project to recover $48,000 over the next year from loss
• Over 5 year period, for every $1 million invested, recovers additional
$110,000 that can continue to be reinvested
• Actions already taken
• Implement the model the risk model for interest rates
• Change policy to ask for borrower allowed interest rates again
• Actions to be taken
• Figure out policy change that allowed for risky population

22. About Jack Chai
From wikipedia