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Debt Repayment


  • Model prepayment risk and automate document classification to predict repayments/prepayment risks of high interest debt (eg, credit debt) and fixed-income assets (eg, mortgage backed securities)

  • Predict whether or not someone will apply for a mortgage over given time periods

  • Score debt to reduce repayment and default risk


  • Achieved predictive accuracy of repayment model over 76%

  • Reduced model deployment time by 70%

Business Value

  • identified $10M of debt retired for single customer

  • Reduced risk of mortgage prepayment due to the loss of future interest payments and the creation of complications in the refinancing strategies. 

H2O's AI and Data Approaches

  • Artificial neural network models, which are applied to predict the prepayment rate on pools of mortgage loans. 

  • Logistic regression for predicting categorical outcomes, using a number of explanatory variables to predict the values of the target variable that can be either binomial or multinomial.

  • Model interpretability by conducting a sensitivity analysis to determine how input variables affect the output of the model.


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