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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In 2014, actual interest rates did not correlate with loss risk

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Desired TrendIdeally, interest rates would increase with increasing loss risk

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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 cut losses in 2014 by $17K or 89%

Minimal increase in average interest rate from 6% to 6.8%Would have cut losses in 2014 by $17K or 89% Would have cut losses from 2009 onwards by $240K or 82%

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

• Basic probability theory to deal with class bias

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

• Basic probability theory to deal with class bias

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

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

• Basic probability theory to deal with class bias

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

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

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

Higher Risk Associated with Borrowers who entered between August 2012 and August 2013

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Higher Risk Associated with Borrowers who entered between August 2012 and August 2013

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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 (cut losses by 89%)

• Impact/Significance• Project to cut losses by $48,000 over the next year• 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

• Actions to be taken• Find policy change that allowed for risky population

Conclusions

About Jack Chai

From wikipedia

Borrower determined maximum interest rate is also correlated with risk

Next things to do

• Incorporate fraud risk• Incorporate a risk of default (this is likely based upon sift score or

some other metric)• Look at interacting terms (type of business/country)