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

Adrian Gepp, J. Holton Wilson, Kuldeep Kumar, Sukanto Bhattacharya

Research output: Contribution to journalArticleResearchpeer-review

36 Downloads (Pure)

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 involv-ing financial economists, forensic accountants and computational modellers. Some of the computational techniques popularly used in the context of fi-nancial 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 compara-tive 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.
Original languageEnglish
Pages (from-to)537-561
Number of pages25
JournalJournal of Data Science
Volume10
Issue number3
Publication statusPublished - 1 Jul 2012

Fingerprint

Insurance
Decision trees
Data mining
Industry

Cite this

@article{c107eb80120849dfa4112e6f4d3420f4,
title = "A comparative analysis of decision trees vis-a-vis other computational data mining techniques in automotive insurance fraud detection",
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 involv-ing financial economists, forensic accountants and computational modellers. Some of the computational techniques popularly used in the context of fi-nancial 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 compara-tive 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.",
author = "Adrian Gepp and Wilson, {J. Holton} and Kuldeep Kumar and Sukanto Bhattacharya",
year = "2012",
month = "7",
day = "1",
language = "English",
volume = "10",
pages = "537--561",
journal = "Journal of Data Science",
issn = "1680-743X",
number = "3",

}

A comparative analysis of decision trees vis-a-vis other computational data mining techniques in automotive insurance fraud detection. / Gepp, Adrian; Wilson, J. Holton; Kumar, Kuldeep; Bhattacharya, Sukanto.

In: Journal of Data Science, Vol. 10, No. 3, 01.07.2012, p. 537-561.

Research output: Contribution to journalArticleResearchpeer-review

TY - JOUR

T1 - A comparative analysis of decision trees vis-a-vis other computational data mining techniques in automotive insurance fraud detection

AU - Gepp, Adrian

AU - Wilson, J. Holton

AU - Kumar, Kuldeep

AU - Bhattacharya, Sukanto

PY - 2012/7/1

Y1 - 2012/7/1

N2 - 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 involv-ing financial economists, forensic accountants and computational modellers. Some of the computational techniques popularly used in the context of fi-nancial 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 compara-tive 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.

AB - 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 involv-ing financial economists, forensic accountants and computational modellers. Some of the computational techniques popularly used in the context of fi-nancial 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 compara-tive 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.

M3 - Article

VL - 10

SP - 537

EP - 561

JO - Journal of Data Science

JF - Journal of Data Science

SN - 1680-743X

IS - 3

ER -