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Identifying sustainable interest rates while helping African small businesses grow
Jack ChaiInsight Data Science Fellow
2014
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sity
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
ensi
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)
β’ Basic probability theory to deal with class bias
Predictive model created from combination of logistic regression and machine learning (SVM)
β’ Basic probability theory to deal with class bias
π πππ π = π πππππ’ππ‘ β (1 β π π ππππππ¦ππππ‘ πππππ’ππ‘ )
Predictive model created from combination of logistic regression and machine learning (SVM)
β’ Basic probability theory to deal with class bias
π πππ π = π πππππ’ππ‘ β (1 β π π ππππππ¦ππππ‘ πππππ’ππ‘ )
Predictive model created from combination of logistic regression and machine learning (SVM)
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sity
β’ Basic probability theory to deal with class bias
β’ Logistic regression identified 4 features that could predict riskβ’ βRiskier populationβ
β’ Borrower allowed maximum interest rate
β’ Loan Category
β’ Country of applicant
Predictive model created from combination of logistic regression and machine learning (SVM)
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sity
β’ Basic probability theory to deal with class bias
β’ Logistic regression identified 4 features that could predict riskβ’ βRiskier populationβ
β’ Borrower allowed maximum interest rate
β’ Loan Category
β’ 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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Au
gust
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12
Au
gust
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13
Predictive model created from combination of logistic regression and machine learning (SVM)
β’ Basic probability theory to deal with class bias
β’ Logistic regression identified 4 features that could predict riskβ’ βRiskier populationβ
β’ Borrower allowed maximum interest rate
β’ Loan Category
β’ Country of applicant
β’ Used identified features to train
kernel SVM with 10 fold cross validation
(89% loss recovery)
β’ Impact/Significanceβ’ Project to recover $48,000 over the next year from loss
β’ Over 5 year period, for every $1 million invested, recovers additional $110,000 that can continue to be reinvested
β’ Actions already takenβ’ Implement the model the risk model for interest rates
β’ Change policy to ask for borrower allowed interest rates again
β’ Actions to be takenβ’ Find policy change that allowed for risky population
Conclusions
About Jack Chai
From wikipedia