This document discusses identifying sustainable interest rates for small businesses in Africa to minimize losses while allowing growth. It finds that in 2014, actual interest rates did not scale with loss risk, and that a minimal increase from 6% to 6.8% could have minimized losses. A predictive model was created using logistic regression and machine learning to identify features like population risk, maximum interest rates, loan categories and countries that can predict risk of default. Implementing this model could recover $48,000 in losses annually and save over $100,000 over 5 years.

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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In 2014, actual interest rates did not scale with loss risk
Actual Trend in 2014

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Actual Trend in 2014
Desired Trend
Ideally, interest rates would increase with increasing loss risk

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9. Minimal increase in average interest rate from 6% to 6.8%
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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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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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12. Predictive model created from combination of logistic regression and machine learning (SVM)
•Basic probability theory to deal with class bias

13. Predictive model created from combination of logistic regression and machine learning (SVM)
•Basic probability theory to deal with class bias
푃푙표푠푠=푃푑푒푓푎푢푙푡∗(1−푃푠표푚푒푝푎푦푚푒푛푡푑푒푓푎푢푙푡)

14. Predictive model created from combination of logistic regression and machine learning (SVM)
•Basic probability theory to deal with class bias
푃푙표푠푠=푃푑푒푓푎푢푙푡∗(1−푃푠표푚푒푝푎푦푚푒푛푡푑푒푓푎푢푙푡)

15. Predictive model created from combination of logistic regression and machine learning (SVM)
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•Basic probability theory to deal with class bias
•Logistic regression identified 4 features that could predict risk
•“Riskier population”
•Borrower allowed maximum interest rate
•Loan Category
•Country of applicant

16. Predictive model created from combination of logistic regression and machine learning (SVM)
Density
•Basic probability theory to deal with class bias
•Logistic regression identified 4 features that could predict risk
•“Riskier population”
•Borrower allowed maximum interest rate
•Loan Category
•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)
•Basic probability theory to deal with class bias
•Logistic regression identified 4 features that could predict risk
•“Riskier population”
•Borrower allowed maximum interest rate
•Loan Category
•Country of applicant
•Used identified features to train
kernel SVM with 10 fold cross validation
(89% loss recovery)

20. •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
•Find policy change that allowed for risky population
Conclusions

21. About Jack Chai
From wikipedia