Identifying sustainable interest rates while helping African small businesses grow

Jack ChaiInsight Data Science Fellow2014

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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

Actual Trend in 2014

Desired Trend

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Minimal increase in average interest rate from 6% to 6.8%D

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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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Predictive model created from combination of logistic regression and machine learning (SVM)

• Logistic regression identified several features that could predict risk

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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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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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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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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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

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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

𝑃 (𝑙𝑜𝑠𝑠 )=𝑃 (𝑑𝑒𝑓𝑎𝑢𝑙𝑡 )∗(1−𝑃 (𝑠𝑜𝑚𝑒𝑝𝑎𝑦𝑚𝑒𝑛𝑡|𝑑𝑒𝑓𝑎𝑢𝑙𝑡 )) Den

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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

𝑃 (𝑙𝑜𝑠𝑠 )=𝑃 (𝑑𝑒𝑓𝑎𝑢𝑙𝑡 )∗(1−𝑃 (𝑠𝑜𝑚𝑒𝑝𝑎𝑦𝑚𝑒𝑛𝑡|𝑑𝑒𝑓𝑎𝑢𝑙𝑡 )) Den

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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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• 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

Conclusions

About Jack Chai

From wikipedia