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An analysis of loan prepayment using competing risks random forests

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Loan prepayment is a large cause of loss to financial institutions when they issue installment loans, and has not been well studied with respect to predicting it for individual borrowers. Using a dataset of competing risks times for loan termination, competing risks random forests were used as a non-parametric approach for identifying useful predictors, and for finding a tuned model that demonstrated that loan prepayment can be predicted on an individual borrower basis. In addition, a new software package we developed, largeRCRF, is introduced and evaluated for the purpose of training competing risks random forests on large scale datasets. This research is a firm first step for financial institutions to reduce their prepayment rates and increase their margins.
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