Abstract
References
- [1] Ribeiro R., Silva B., Pimenta C., & Poeschl G., 2020. Why do consumers perpetrate fraudulent behaviors in insurance?. Crime, Law and Social Change, 73(3), pp. 249-273.
- [2] Abdallah A., Maarof M. A., & Zainal A., 2016. Fraud detection system: A survey. Journal of Network and Computer Applications, 68, pp. 90-113.
- [3] Hargreaves C. A., & Singhania V., 2015. Analytics for Insurance Fraud Detection: An Empirical Study. American Journal of Mobile Systems, Applications and Services, 1(3), pp. 223-232.
- [4] Liu X., Yang J. B., Xu D. L., 2020. Fraud detection in automobile insurance claims: a statistical review. In: Developments of Artificial Intelligence Technologies in Computation and Robotics: Proceedings of the 14th International FLINS Conference, pp. 1003-1012.
- [5] Patil K. S., Godbole A., 2018. A survey on machine learning techniques for insurance fraud prediction. Helix, 8(6), pp. 4358-4363.
- [6] Sumalatha M. R., Prabha M., 2019. Mediclaim fraud detection and management using predictive analytics. In: Proc. of Intl. Conference on Computational Intelligence and Knowledge Economy, pp. 517-522.
- [7] Sowah R. A., Kuuboore M., Ofoli A., Kwofie S., Asiedu L., Koumadi K. M., Apeadu K. O., 2019. Decision support system for fraud detection in health insurance claims using genetic support vector machines. Journal of Engineering, Article ID 1432597.
- [8] Kalwihura J. S., Logeswaran R., 2020. Auto-insurance fraud detection: a behavioral feature engineering approach. Journal of critical reviews, 7(3), pp. 125-129.
- [9] Gomes C., Jin Z., Yang H., 2021. Insurance fraud detection with unsupervised deep learning. Journal of Risk and Insurance, 88, pp. 591–624.
- [10] Severino M. K., & Peng Y., 2021. Machine learning algorithms for fraud prediction in property insurance: Empirical evidence using real-world microdata. Machine Learning with Applications, 5, 100074.
- [11] Rukhsar L., Bangyal W. H., Nisar K., & Nisar S., 2022. Prediction of insurance fraud detection using machine learning algorithms. Mehran University Research Journal of Engineering & Technology, 41(1), pp. 33-40.
- [12] Baesens B., Van Vlasselaer V., Verbeke W., 2015. Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques. John Wiley & Sons, Inc
- [13] Katoch S., Chauhan S.S. & Kumar V., 2021. A review on genetic algorithm: past, present, and future. Multimedia Tools and Applications, 80, pp. 8091-8126.
- [14] Dokas P., Ertoz L., Kumar V., Lazarevic A., Srivastava J., & Tan P. N., 2002. Data mining for network intrusion detection. In: Proc. of NSF Workshop on Next Generation Data Mining (pp. 21-30).
Abstract
Fraud detection identifies suspicious activities, false pretenses, wrongful or criminal deception intended to result in financial gain. Fraud is rare, well thought, effortful, and deceiving throughout claims. Detecting fraudulent claims is essential for the insurance industry. Therefore, most insurance companies must devote time and budget to fraud detection. Fraud detection can be divided into two categories; the main and most common type of fraud is individual fraud. Individual frauds can appear in many kinds of forms. For example, damage to an asset might be occurred before issuing a policy and be reported after. The second category is organized fraud which is much rarer and harder to detect than individual fraud. Especially motor insurance fraud is commonly attempted by organized crime rings. Counterparties involved in fraudulent claims change frequently, and changes make fraud detection difficult. According to Insurance Information and Monitoring Center findings, the fraudulent claim payment ratio is 10 to 30 %, and the detection success rate for an individual is at 1.4 to 5%. At the same time, the annual fraud cost is at 200 to 300 $ million. This study proposes a fraud detection platform called SOBE, which assists fraud departments’ claim inquiry more easily and shorter than manual investigation made by employees. At its core, SOBE uses a rule engine approach. In order to support the rule engine, there is also a machine learning algorithm for fraud detection. In addition, the SNA module detects interconnected fraud counterparts among claim files. Consequently, the SOBE fraud detection platform allows Anadolu Sigorta to prevent improper payments from claiming participants. SOBE platform, the central fraud detection platform at Anadolu Sigorta, was developed in-house using different technologies and methods, including KNIME Analytics Platform, Python, graph methods, and web service methodologies.
References
- [1] Ribeiro R., Silva B., Pimenta C., & Poeschl G., 2020. Why do consumers perpetrate fraudulent behaviors in insurance?. Crime, Law and Social Change, 73(3), pp. 249-273.
- [2] Abdallah A., Maarof M. A., & Zainal A., 2016. Fraud detection system: A survey. Journal of Network and Computer Applications, 68, pp. 90-113.
- [3] Hargreaves C. A., & Singhania V., 2015. Analytics for Insurance Fraud Detection: An Empirical Study. American Journal of Mobile Systems, Applications and Services, 1(3), pp. 223-232.
- [4] Liu X., Yang J. B., Xu D. L., 2020. Fraud detection in automobile insurance claims: a statistical review. In: Developments of Artificial Intelligence Technologies in Computation and Robotics: Proceedings of the 14th International FLINS Conference, pp. 1003-1012.
- [5] Patil K. S., Godbole A., 2018. A survey on machine learning techniques for insurance fraud prediction. Helix, 8(6), pp. 4358-4363.
- [6] Sumalatha M. R., Prabha M., 2019. Mediclaim fraud detection and management using predictive analytics. In: Proc. of Intl. Conference on Computational Intelligence and Knowledge Economy, pp. 517-522.
- [7] Sowah R. A., Kuuboore M., Ofoli A., Kwofie S., Asiedu L., Koumadi K. M., Apeadu K. O., 2019. Decision support system for fraud detection in health insurance claims using genetic support vector machines. Journal of Engineering, Article ID 1432597.
- [8] Kalwihura J. S., Logeswaran R., 2020. Auto-insurance fraud detection: a behavioral feature engineering approach. Journal of critical reviews, 7(3), pp. 125-129.
- [9] Gomes C., Jin Z., Yang H., 2021. Insurance fraud detection with unsupervised deep learning. Journal of Risk and Insurance, 88, pp. 591–624.
- [10] Severino M. K., & Peng Y., 2021. Machine learning algorithms for fraud prediction in property insurance: Empirical evidence using real-world microdata. Machine Learning with Applications, 5, 100074.
- [11] Rukhsar L., Bangyal W. H., Nisar K., & Nisar S., 2022. Prediction of insurance fraud detection using machine learning algorithms. Mehran University Research Journal of Engineering & Technology, 41(1), pp. 33-40.
- [12] Baesens B., Van Vlasselaer V., Verbeke W., 2015. Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques. John Wiley & Sons, Inc
- [13] Katoch S., Chauhan S.S. & Kumar V., 2021. A review on genetic algorithm: past, present, and future. Multimedia Tools and Applications, 80, pp. 8091-8126.
- [14] Dokas P., Ertoz L., Kumar V., Lazarevic A., Srivastava J., & Tan P. N., 2002. Data mining for network intrusion detection. In: Proc. of NSF Workshop on Next Generation Data Mining (pp. 21-30).
Details
| Primary Language | English |
|---|---|
| Subjects | Artificial Intelligence, Software Engineering |
| Journal Section | Articles |
| Authors | |
| Early Pub Date | June 30, 2022 |
| Publication Date | November 30, 2022 |
| Acceptance Date | March 3, 2022 |
| Published in Issue | Year 2022 Volume: 5 Issue: ICOLES2021 Special Issue |