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

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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

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  • Student Theses

    Financial statement fraud detection using supervised learning methods

    Author: Gepp, A., 10 Oct 2015

    Supervisor: Kumar, K. (Supervisor) & Bhattacharya, S. (External person) (Supervisor)

    Student thesis: Doctoral Thesis

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