Fraud Detection Using a Fuzzy Expert System In Motor Insurance

Document Type : Research Paper

Authors

1 Associate Prof. in Industrial Management, Faculty of Management and Accounting, Allameh Tabataba'i University, Tehran, Iran

2 MSc. in IT Management, Faculty of Management and Accounting, Allameh Tabataba'i University, Tehran, Iran

Abstract

Insurance industry experts believe that fraud is a destructive disaster in the insurance industry. Over the years, many methods have been used in the literature for fraud detection, one of which is expert systems. Fraud detection expert systems are based on the knowledge of experts in the field of insurance identify fraud. Judgment of experts is mostly based on evidence, documents, qualitative information which is often presented in verbal words to describe the fraudulent behavior. In the presented model, 61 qualitative and quantitative criteria related to the detection of fraud in car insurance were identified. Then, these criteria were prioritized according to expert opinion and 17 criteria with the highest priority classified into eight factors were selected. In the suggested system fuzzy inference was performed using Mamdani algorithm. Finally, the designed system was implemented to an Iranian private insurance company and the validity of the system assessed by a questionnaire and came up to 69.45%. The obtained results indicate that the proposed model is able to detect the fraud quite significantly.

Keywords

Main Subjects

References

Belhadji E.B. & Dionne, G. (1997). Development of an Expert System for the Automatic Fraud Detection of Automobile Insurance Fraud. Canada, Montreal: Ecole des Hutes Etudes Commerciales.
Bhargava, B., Zhong, Y. & Lu, Y. (2003). Fraud Formalization and Detection. Lecture Notes in Computer Science, 2737: 330-339.
Derrig, R.A., Johnston, D.J. & Sprinkel, A.E. (2006). Auto Insurance Fraud: Measurements and Efforts to Combat It. Risk Management and Insurance Review, 9 (2): 109-130.
Firouzi, M., Shokri, M., Kazemi, L., Zahedi, S. (2011). Fraud Detection in Car Insurance using Data Mining Techniques. Insurance Journal, 26(3): 103-128. (in Persian)
Jafari, Z. (2013). Fraud Detection in Car Insurance using a Fuzzy Expert System. MA Thesis, Allameh Tabatabai University. (in Persian)
Mamdani, E.H. & Assilian, S. (1975). An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller. International Journal of Man-Machine Studies, 7(1): 1–13.
Ngai, E.W.T., Hu, Y., Wong, Y.H., Chen, Y. & Sun, X. (2010). The Application of Data Mining Techniques in Financial Fraud Detection: A Classification Framework and an Academic Review of literature. Decision Support Systems, 50(3): 559-569.
Taghavifard, M.T., Hosseini F. & Khanbabaei, M. (2014). A Hybrid Credit Scoring Model using Genetic Algorithm and Fuzzy Expert Systems. Journal of IT Management, 6(1): 31-46. (in Persian)
Tennyson, S. & Salsas-Forn, P. (2002). Claims Auditing In Automobile Insurance: Fraud Detection and Deterrence Objectives. The Journal of Risk and Insurance, 69(3): 289–308.
Viaene, S., Ayuso, M., Guillen, M. & Van Gheel, D. (2007). Strategies for Detecting Fraudulent Claims in the Automobile Insurance Industry. European Journal of Operational Research, 176 (1): 565-583.
Yaghmaei, F. (2000). Content validity and its measurement . Journal of Nursing and Midwifery, 10 (33): -35-39.
Zhang, N. &  Xu, W. (2000). A comparative study on sufficient conditions for Takagi-Sugeno fuzzy systems as universal approximators. IEEE Transactions, 8 (6): 773 – 780.
 

Files

Share

How to cite

Statistics