An analysis of loan prepayment using competing risks random forests

Author: 
Date created: 
2019-11-27
Identifier: 
etd20603
Keywords: 
Installment loans
Prepayment
Competing risks
Random forests
LargeRCRF
Abstract: 

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.

Document type: 
Graduating extended essay / Research project
Rights: 
This thesis may be printed or downloaded for non-commercial research and scholarly purposes. Copyright remains with the author.
File(s): 
Supervisor(s): 
Jiguo Cao
Department: 
Science: Department of Statistics and Actuarial Science
Thesis type: 
(Project) M.Sc.
Statistics: