A Comparative Analysis of Decision Trees Vis-'a-vis Other Computational Data Mining Techniques in Automotive Insurance Fraud Detection
| Autor: | Sukanto Bhattacharya, Kuldeep Kumar, J. Holton Wilson, Adrian Gepp |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
0301 basic medicine
Insurance fraud Computer science business.industry Decision tree Automotive industry Context (language use) computer.software_genre Insurance claims 03 medical and health sciences 030104 developmental biology 0302 clinical medicine 030220 oncology & carcinogenesis Business failure prediction Data mining business Insurance industry Financial fraud computer |
| Zdroj: | Bond University |
| ISSN: | 1683-8602 1680-743X |
| DOI: | 10.6339/jds.201207_10(3).0010 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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