Development of an Early Warning System for Insurance Fraud Detection Using Statistical Analytics
DOI:
https://doi.org/10.70062/jiafc.v1i3.207Keywords:
Insurance Fraud, Early Warning System, Statistical Analytics, Machine Learning, Fraud DetectionAbstract
This study focuses on the development of an Early Warning System (EWS) for detecting insurance fraud using statistical analytics and machine learning approaches. Insurance fraud, particularly false claims, causes substantial financial losses and weakens the credibility of insurance institutions. The objective of this research is to design a proactive detection model capable of identifying fraudulent claims at the earliest possible stage. The study employs a quantitative experimental approach using a labeled dataset of insurance claims. Statistical regression analysis and classification algorithms, including Logistic Regression, Decision Tree, Random Forest, and Support Vector Machine (SVM), were implemented to analyze key variables such as claim amount, claim frequency, and customer behavior. The results show that Random Forest achieved the highest performance, effectively differentiating between fraudulent and legitimate claims. The developed EWS successfully reduced fraud detection time and improved predictive accuracy compared to conventional post-claim investigations. Overall, this research demonstrates that the integration of statistical and machine learning methods provides a more efficient, scalable, and adaptive solution for preventing insurance fraud and safeguarding financial integrity within the insurance sector.
References
Achary, R., Shelke, C. J., & Shrivastava, V. K. (2025). Insurance claim fraud detection using Benford’s method and machine learning. ICICV.
Agarwal, R., Kalsi, D., Jain, P., Gupta, P., & Goel, R. (2025). Car insurance fraud detection using machine learning models. IEEE NGISE.
Agarwal, R., Kalsi, D., Jain, P., Gupta, P., & Goel, R. (2025). Car insurance fraud detection using machine learning models. IEEE NGISE 2025 – International Conference on Next Generation Information System Engineering. https://doi.org/10.1109/NGISE64126.2025.11085234
Ashok, P., & Durge, A. S. (2025). Fraud detection and prevention in healthcare insurance claims using machine learning regression models. ICDSBS.
Banulescu-Radu, D., & Kougblenou, Y. (2024). Data science for insurance fraud detection: A review. In Handbook of Insurance (Vol. I, 3rd ed., pp. 417–446). Springer. https://doi.org/10.1007/978-3-031-69561-2_15
Baştürk, F. H. (2020). Insurance fraud: The case in Turkey. Contemporary Studies in Economic and Financial Analysis, 102, 77–97. https://doi.org/10.1108/S1569-375920200000102009
Button, M., Brooks, G., Lewis, C., & Aleem, A. (2017). Explaining ‘cash-for-crash’ insurance fraud in the United Kingdom. Australian and New Zealand Journal of Criminology, 50(2), 176–194. https://doi.org/10.1177/0004865816638910
Carracedo, P., & Hervás, D. (2025). Models for insurance fraud detection: Dealing with unbalanced data. Lecture Notes in Computer Science, 14779, 3–9.
Chauhan, R., Negi, R., & Verma, D. K. (2023). Analysis of machine learning algorithms for insurance fraud detection. IEEE R10-HTC.
Chitteti, C., Yamuna, M., Srinath, M., Govardhan, C., & Vignatha, A. (2025). Healthcare insurance fraud detection using machine learning. ICOEI.
Do, N.-T., Tan, L. D., Le, D. K., & Nguyen, Q.-H. (2024). Addressing data imbalance in insurance fraud prediction using sampling techniques and robust losses. Lecture Notes on Data Engineering and Communications Technologies, 230, 361–371.
Doulat, A. A., Ayo-Bali, O. E., & Shaik, S. (2025). Fraud detection in insurance claims using supervised machine learning models. SmartNets 2025.
Esna-Ashari, M., Khamesian, F., & Khanizadeh, F. (2022). Using local outlier factor to detect fraudulent claims in auto insurance. Journal of Mathematics and Modeling in Finance, 2(1), 167–182.
Gheysarbeigi, A., Rakhshaninejad, M., Fathian, M., & Barzinpour, F. (2025). An ensemble-based auto insurance fraud detection using BQANA hyperparameter tuning. IEEE Access, 13, 42997–43012.
Hanafy, M., & Ming, R. (2021). Using machine learning models to compare various resampling methods in predicting insurance fraud. Journal of Theoretical and Applied Information Technology, 99(12), 2819–2833.
Hong, B., Lu, P., Xu, H., Lu, J., Lin, K., & Yang, F. (2024). Health insurance fraud detection based on multi-channel heterogeneous graph structure learning. Heliyon, 10(9), e30045. https://doi.org/10.1016/j.heliyon.2024.e30045
Jing, Z. (2022). Fabricating insurance subject matter and defrauding insurance money: A civil wrong or a criminal offence? Asia Pacific Law Review, 30(1), 145–166. https://doi.org/10.1080/10192557.2022.2045710
Kaushik, P., Rathore, S. P. S., Bisen, A. S., & Rathore, R. (2024). Enhancing insurance claim fraud detection through advanced data analytics techniques. IEEE Region 10 Symposium (TENSYMP).
Majumder, R. Q. (2025). Designing an intelligent fraud detection system for healthcare insurance claims using a machine learning approach. GINOTECH 2025.
Ming, R., Mohamad, O., Innab, N., & Hanafy, M. (2024). Bagging vs. boosting in ensemble machine learning? An integrated application to fraud risk analysis in the insurance sector. Applied Artificial Intelligence, 38(1).
Na Bangchang, K., Wongsai, S., & Simmachan, T. (2023). Application of data mining techniques in automobile insurance fraud detection. ACM International Conference Proceeding Series, 48–55. https://doi.org/10.1145/3613347.3613355
Obodoekwe, N., & van der Haar, D. T. (2019). A comparison of machine learning methods applicable to healthcare claims fraud detection. Advances in Intelligent Systems and Computing, 918, 548–557. https://doi.org/10.1007/978-3-030-11890-7_53
Owolabi, T., Shahra, E. Q., & Basurra, S. (2024). Auto-insurance fraud detection using machine learning classification models. Lecture Notes in Networks and Systems, 695, 503–513.
Ramesar, N., Ramoudith, S., Sharma, N., & Hosein, P. (2023). A cost-minimization approach to automobile insurance fraud detection. IEEE ICTMOD 2023 – International Conference on Technology Management, Operations and Decisions. https://doi.org/10.1109/ICTMOD59086.2023.10438120
Saddi, V. R., Boddu, S., Gnanapa, B., Jiwani, N., & Kiruthiga, T. (2024). Leveraging big data and AI for predictive analysis in insurance fraud detection. ICICACS.
Saddi, V. R., Gnanapa, B., Boddu, S., Jiwani, N., & Logeshwaran, J. (2023). An intelligent analysis of miscellaneous behavior and fraud detection in CVD diagnosis insurance claims data using deep learning framework. IEEE TEMSCON-ASPAC 2023. https://doi.org/10.1109/TEMSCON-ASPAC59527.2023.10531373
Samara, B. (2024). Using binary logistic regression to detect health insurance fraud. Pakistan Journal of Life and Social Sciences, 22(2), 11184–11198. https://doi.org/10.57239/PJLSS-2024-22.2.00848
Simmachan, T., Manopa, W., Neamhom, P., Poothong, A., & Phaphan, W. (2023). Detecting fraudulent claims in automobile insurance policies by data mining techniques. Thailand Statistician, 21(3), 552–568.
Tongesai, M., Mbizo, G., & Zvarevashe, K. (2022). Insurance fraud detection using machine learning. ZCICT.
Tseng, L.-M. (2019). Customer insurance frauds: The influence of fraud type, moral intensity and fairness perception. Managerial Finance, 45(3), 452–467. https://doi.org/10.1108/MF-04-2018-0162
Varadi, P., Lukacs, J., & Horvath, R. (2023). Examination of vehicle fraud detection possibilities with the help of fuzzy inference system. IEEE SACI 2023 – 17th International Symposium on Applied Computational Intelligence and Informatics, 353–358. https://doi.org/10.1109/SACI58269.2023.10158631
Vemulapalli, G. (2024). Fighting fraud with algorithms: AI solutions for claim detection and revolutionizing fraud detection in insurance. In Artificial Intelligence and Machine Learning for Sustainable Development (pp. 125–140). CRC Press. https://doi.org/10.1201/9781003497189-10
Yu, M. (2025). Automobile accident claims fraud prediction based on machine learning. IEEE PRMVAI.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Journal of Investigative Auditing & Financial Crime
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
