Deakin University
Browse

File(s) under permanent embargo

A comparative analysis of decision trees vis-à-vis other computational data mining techniques in automotive insurance fraud detection

journal contribution
posted on 2012-07-01, 00:00 authored by A Gepp, J Wilson, K Kumar, Sukanto BhattacharyaSukanto Bhattacharya
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.

History

Journal

Journal of data science

Volume

10

Issue

3

Pagination

537 - 561

Publisher

Columbia University : Department of Statistics

Location

New York, N.Y.

ISSN

1680-743X

eISSN

1683-8602

Language

eng

Publication classification

C1 Refereed article in a scholarly journal

Copyright notice

2012, Journal of Data Science