Date of this Version

7-1-2012

Document Type

Journal Article

Publication Details

Published version

Gepp, A., Wilson, J. H., Kumar, K., & Bhattacharya, S. (2012). A comparative analysis of decision trees vis-a-vis other computational data mining techniques in automotive insurance fraud detection. Journal of Data Science, 10(3), 537-561.

Access the journal

ISSN

1680-742X

Abstract

The development and application of computational data mining techniques in financial fraud detection and business failure prediction has become a popular cross-disciplinary research area in recent times involving financial economists, forensic accountants and computational modellers. Some of the computational techniques popularly used in the context of financial fraud detection and business failure prediction can also be effectively applied in the detection of fraudulent insurance claims and therefore, can be of immense practical value to the insurance industry. We provide a comparative analysis of prediction performance of a battery of data mining techniques using real-life automotive insurance fraud data. While the data we have used in our paper is US-based, the computational techniques we have tested can be adapted and generally applied to detect similar insurance frauds in other countries as well where an organized automotive insurance industry exists.

Share

COinS
 

This document has been peer reviewed.

 

To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.