Applying Classification and Regression Trees to manage financial risk

Repository

Applying Classification and Regression Trees to manage financial risk

Show full record

Title: Applying Classification and Regression Trees to manage financial risk
Author: Martin, Stephen Fredrick
Abstract: This goal of this project is to develop a set of business rules to mitigate risk related to a specific financial decision within the prepaid debit card industry. Under certain circumstances issuers of prepaid debit cards may need to decide if funds on hold can be released early for use by card holders prior to the final transaction settlement. After a brief introduction to the prepaid card industry and the financial risk associated with the early release of funds on hold, the paper presents the motivation to apply the CART (Classification and Regression Trees) method. The paper provides a tutorial of the CART algorithms formally developed by Breiman, Friedman, Olshen and Stone in the monograph Classification and Regression Trees (1984), as well as, a detailed explanation of the R programming code to implement the RPART function. (Therneau 2010) Special attention is given to parameter selection and the process of finding an optimal solution that balances complexity against predictive classification accuracy when measured against an independent data set through a cross validation process. Lastly, the paper presents an analysis of the financial risk mitigation based on the resulting business rules.
Department: Mathematics
Subject: CART Classification and Regression Trees Breiman Risk Prepaid Debit cards Rollback R RPART Cross validation
URI: http://hdl.handle.net/2152/ETD-UT-2012-05-5428
Date: 2012-05

Files in this work

Size: 1.582Mb
Format: application/pdf

This work appears in the following Collection(s)

Show full record


Advanced Search

Browse

My Account

Statistics

Information