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http://dx.doi.org/10.14400/JDC.2022.20.2.241

Bike Insurance Fraud Detection Model Using Balanced Randomforest Algorithm  

Kim, Seunghoon (Korea Research Institute for Human Settlements)
Lee, Soo Il (Division of Transportation Safety, Coupang Corportation)
Kim, Tae ho (Division of Transportation Safety Planning, Coupang Corportation)
Publication Information
Journal of Digital Convergence / v.20, no.2, 2022 , pp. 241-250 More about this Journal
Abstract
Due to the COVID-19 pandemic, with increased 'untact' services and with unstable household economy, the bike insurance fraud is expected to surge. Moreover, the fraud methodology gets complicated. However, the fraud detection model for bike insurance is absent. we deal with the issue of skewed class distribution and reflect the criterion of fraud detection expert. We utilize a balanced random-forest algorithm to develop an efficient bike insurance fraud detection model. As a result, while the predictive performance of balanced random-forest model is superior than it of non-balanced model. There is no significant difference between the variables used by the experts and the confirmatory models. The important variables to detect frauds are turned out to be age and gender of driver, correspondence between insured and driver, the amount of self-repairing claim, and the amount of bodily injury liability.
Keywords
Platform Delivery Service; Bike insurance fraud detection Balanced random-forest algorithm; Imbalanced data;
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Times Cited By KSCI : 2  (Citation Analysis)
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1 Wen, C.-H., Wang, M.-J., & Lan, L. W. (2005). Discrete choice modeling for bundled automobile insurance policies. Journal of the Eastern Asia Society for Transportation Studies, 6. 1914-1928.
2 H. G. Jo. (1990). The Cause of Insurance Fraud And Countermeasures. Korean Journal of Insurance, 35, 75-102
3 H. G. Jo. (2001). Countermeasures of Insurance Fraud For Nation. Journal of Insurance Studies, 12(2).
4 C. Y. Kim. (1996). Case Study of the Type of Car Insurance Frauds, General Insurance Association of Korea, 328, 43-61.
5 H. S. Kim. (1999). Brief Study of The Development of Automobile insurance Fraud Early-Warning model, General Insurance, 363, 68-80.
6 H. S. Kim. (2000). A Study on The Development of Automobile insurance Fraud Early-Warning model using Claim Adjusters' Expert knowledge. The Journal of Risk management, 16, 59-97.
7 Abdallah, A., Maarof, M. A., & Zainal, A. (2016). Fraud detection system: A survey. Journal of Network and Computer Applications, 68, 90-113.   DOI
8 T. H. Kim, J. I. Lim. (2020). A Study on Conspired Insurance Fraud Detection Modeling Using Social Network Analysis, Journal of the Korea Society of Computer and Information, 25(3), 117-127.   DOI
9 Martino Scheepens. (retrieved on 11.30.2021). Coronavirus, what have you done?. FRISS. https://www.friss.com/blog/coronavirus-what-have-you-done/
10 Agjee, N. H., Mutanga, O., Peerbhay, K., & Ismail, R. (2018). The impact of simulated spectral noise on random forest and oblique random forest classification performance. Journal of Spectroscopy. 2018.8
11 Brennan, P. (2012). A comprehensive survey of methods for overcoming the class imbalance problem in fraud detection. Thesis, (June), 1-107.
12 Chen, C., Liaw, A., & Breiman, L. (2004). Using Random Forest to Learn Imbalanced Data. In Department of Statistics, UC berkeley.
13 Brockett, P. L., Derrig, R. a, Golden, L. L., & Alpert, M. (2002). Fraud Classification Using Principal Component Analysis of RIDITs. The Journal of Risk and Insurance, 69(3), 341-371.   DOI
14 Viaene, S., Ayuso, M., Guillen, M., Van Gheel, D., & Dedene, G. (2007). Strategies for detecting fraudulent claims in the automobile insurance industry. European Journal of Operational Research, 176(1), 565-583.   DOI
15 T. K. Sung. (2003). Detection of Insurance Fraud using Visualization Data Mining Tool. Information System Review, 5(1), 49-60.
16 Fernandez, A., Garcia, S., Herrera, F., & Chawla, N. V. (2018). SMOTE for Learning from Imbalanced Data: Progress and Challenges, Marking the 15-year Anniversary. Journal of Artificial Intelligence Research, 61, 863-905.   DOI
17 Y. J. Kim. (1998). Case Study of Car Insurance for Moral Hazard, General Insurance, 359, 60-71.
18 H. W. Byun, J. Y. Son. (2020). Prevention of Insurance Fraud Utilizing Data Analysis. KIRI Report (2020.11.23.), 1-7.
19 Roy, R., & George, K. T. (2017). Detecting insurance claims fraud using machine learning techniques. Proceedings of IEEE International Conference on Circuit, Power and Computing Technologies, ICCPCT 2017.
20 Artis, M., Ayuso, M., & Guillen, M. (2002). Detection of Automobile Insurance Fraud With Discrete Choice Models and Misclassified Claims. The Journal of Risk and Insurance, 69(3), 325-340.   DOI
21 G. Y. Gim. (1996). Developing Early Detecting Insurance Fraud System: Fuzzy Theory and AHP, Insurance Development Studies, 18, 4-28.
22 J. D. Kim, J. S. Park. (2006). A Fraud Detection Model for Automobile Insurance Claims. Risk Management. 17(1), 109-152.
23 Sithic, H. L., & Balasubramanian, T. (2013). Survey of Insurance Fraud Detection Using Data Mining Techniques. International Journal of Innovative Technology and Exploring Engineering, 2(3), 62-65.
24 Subelj, L., Furlan, S., & Bajec, M. (2011). An expert system for detecting automobile insurance fraud using social network analysis. Expert Systems with Applications, 38(1), 1039-1052.   DOI
25 Fiorentini, N., & Losa, M. (2020). Handling Imbalanced Data in Road Crash Severity Prediction by Machine Learning Algorithms. Infrastructures, 5(7).
26 Ai, J., Golden, L. L., & Brockett, P. L. (2009). Assessing Consumer Fraud Risk in Insurance Claims. North American Actuarial Journal, 13(4), 438-458.   DOI
27 Matthew J. Smith. (retrieved on 11.30.2021). Insurance Fraud Report (2020). https://knowledge.friss.com/hubfs/Ebooks/Insurance%20Fraud%20Report%202020-2021%20EN.pdf?utm_campaign=Fraud%20Survey&utm_medium=email&_hsmi=98996085&_hsenc=p2ANqtz-9b05tppFd4OvW5Pgn40Us4ktpp0dXzleaTZb8IQV2-j9muWaPkF6WLs3jg2XUdudg0gUyFbZtE6ldFqd8yLfN59MVHA&utm_content=98996085&utm_source=hs_automation
28 M. J. Lee, G. Y. Gim. (2007). An Empirical Study on the Development of Behavior Model of Insurance Fraud. Journal of Information Technology Services, 6(2), 1-18.