Identifying sustainable interest rates while helping African small businesses grow

Jack ChaiInsight 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 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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ty

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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 ($17K and 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)

• 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)

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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

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

Den

sity

• 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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12

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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

(89% loss recovery)

• 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

About Jack Chai

From wikipedia