Post on 15-Jun-2015
description
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
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)
• 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)
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
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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sity
Au
gust
20
12
Au
gust
20
13
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