Presenting a hybrid model for identifying claims of suspicious damages in agricultural insurance

Ahmadlou, Y.; Pourebrahimi, A.; Tanha, J.; Rajabzadeh, A.

doi:10.22056/ijir.2023.01.06

Document Type : Original Research Paper

Authors

¹ Department of Information Technology Management, Science and Research Branch, Islamic Azad University, Tehran, Iran

² Department of Industrial Management, Karaj Branch, Islamic Azad University, Karaj, Iran

³ Department of Information Technology Engineering, Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran

⁴ Department of Industrial Management, Faculty of Management and Economics, Tarbiat Modares University, Tehran, Iran

https://doi.org/10.22056/ijir.2023.01.06

Abstract

BACKGROUND AND OBJECTIVES: It is very difficult and maybe impossible to identify suspicious damage claims in agricultural insurance using traditional methods and using the opinions of experts among a multitude of claims. In the current research, a model for discovering suspicious damage claims in agricultural insurance using data mining techniques has been presented to help the agricultural insurance fund in identifying such claims.
METHODS: The research method in the present research is applied in terms of intention and descriptive-post-event in terms of quiddity. One of the applications of data mining is anomaly detection. In the present study, a technique for detecting anomalies in the data using ensemble machine learning models is carride out. To enforcement this method, real data on compensation paid for wheat insurance (irrigated and rainfed) for one year in Khuzestan province was used. Because of differences in the process of determining damages of irrigated and rainfed wheat insurance policies, their anomalies were analyzed separately and a number of suspicious claims were acquired for each.
FINDINGS: The analysis of the results showed 5 types of suspicious behavior in claiming damages. The ratio of suspicious claims to the total (percentage of anomalies) was estimated using the histogram of anomalous scores and the opinion of insurance fund experts about 1.5%. Suspicious and unusual cases were examined by experts and the final accuracy of the model in correctly identifying suspicious cases was 72% for irrigated wheat insurance and 68% for dryland wheat insurance.
CONCLUSION: Based on the obtianed results, the presented model can be used to detecte suspicious claims in wet and dry wheat insurance policies. Since most of the unusual cases are caused by not providing sufficient documentation, it can be due to the presentation of forging insurance policies or the existence of collusion between the insured, the agent or the assessor. Therefore, more care should be taken in the payment process. The present study was conducted on the product and can be used for other crops as well.

Keywords

Main Subjects

Loss adjuster in insurance

References

Aggarwal, C.C., (2013). Outlier ensembles: Position paper. ACM SIGKDD explorations newsletter., 14(2): 49-58 (10 Pages).

Aggarwal, C.C.; Sahte, S., (2017). Outlier ensembles: An introduction. Springer.

The World Bank (2022). Agriculture & rural development.

Barbado, A.; Corcho, Ó.; Benjamins, R., (2022). Rule extraction in unsupervised anomaly detection for model explainability: Application to OneClass SVM. Expert Syst. Appl., 189(1): 1-20 (20 Pages).

Bauder, R.; Da Rosa, R.; Khoshgoftaar, T., (2018). Identifying medicare provider fraud with unsupervised machine learning. In 2018 IEEE international conference on information reuse and integration (IRI)., 285-292 (8 Pages).

Bose, I.; Mahapatra, R.K., (2001). Business data mining —A machine learning perspective. Inf. Manage., 39(3): 211-225 (15 Pages).

Bouazza, I.; Ameur, E.B.; Ameur, F., (2019). Datamining for fraud detecting, state of the art. Adv. Intell. Syst. Sustainable Dev., 205-219 (15 Pages).

Brownlee, J., (2020). Data preparation for machine learning.

Chandola, V.; Banerjee, A.; Kumar, V., (2009). Anomaly detection: A survey. ACM Comput. Surv., 41(3): 1-58 (58 Pages).

Clayton, C., (2022). FBI touts work on crop insurance fraud case involving kentucky tobacco farmers. Insur. Agents.

Cole, S.A.; Xiong, W., (2017). Agricultural insurance and economic development. Annu. Rev. Econ., 9(1): 235-262 (28 Pages).

Bertoni, D., (2006). Crop insurance more needs to be done to reduce program’s vulnerability to fraud, waste, and abuse. U.S. Gov. Account. Office., 1-18 (18 Pages).

Das, S.; Wong, W.K.; Dietterich, T.; Fern, A.; Emmott, A., (2016). Incorporating expert feedback into active anomaly discovery. In 2016 IEEE 16th international conference on data mining (ICDM)., 853-858 (6 Pages).

Ertekin, S.; Huang, J.; Bottou, L.; Giles, L., (2007). Learning on the border: Active learning in imbalanced data classification. In Proceedings of the 16th ACM conference on conference on information and knowledge management., 127-136 (10 Pages).

Gill, K.M.; Woolley, A.; Gill, M., (2005). Insurance fraud: The business as a victim?. Crime., 73-82 (10 Pages).

Gill, W., (2009). Fighting fraud with advanced analytics. Can. Underwriter. Bus. Inf. Group., 76(9): 28-32 (5 Pages).

Goodarzi, A.; Janatbabaei, S., (2017). Evaluation of Three Data Mining Algorithms (Decision Tree, Naive Bayes, Logistic Regression) in Auto Insurance Fraud Detection. Insur. Res., 1(2): 61-80 (19 Pages). [In Persian]

Gomes, C.; Jin, Z.; Yang, H., (2021). Insurance fraud detection with unsupervised deep learning. J. Risk. Insur., 88(3): 591-624 (34 Pages).

Han, J.; Kamber, M.; Pei, J., (2011). Data mining: Concepts and techniques. Morgan Kaufmann publishers Inc., 1-740 (740 Pages).

Hassan, A.K.I.; Abraham, A., (2016). Modeling insurance fraud detection using ensemble combining classification. Int. J. Comput. Inf. Syst. Indust. Manage. App., 8(1): 257-265 (9 Pages).

Hosseini, A.; Rezaei, A., (2019). Fraud detection and management strategies in the insurance organizations using data mining techniques. Social. Secur. Res. Inst., 14(4): 111-136 (26 Pages). [In Persian]

Kim, D.; Lee, S.; Lee, J., (2020). An ensemble-based approach to anomaly detection in marine engine sensor streams for efficient condition monitoring and analysis. Sensors., 20(24): 1-16 (16 Pages).

Kirlidog, M.; Asuk, C., (2012). A fraud detection approach with data mining in health insurance. Procedia. Social. Behav. Sci., 62(1): 989-994 (6 Pages).

Li, J.; Huang, K.Y.; Jin, J.; Shi, J., (2008). A survey on statistical methods for health care fraud detection. Health care. Manage. Sci., 11(1): 275-287 (13 Pages).

Liu, F.T.; Ting, K.M.; Zhou, Z.H., (2008). Isolation forest. In 2008 8th IEEE international conference on data mining., 413-422 (10 Pages).

Marzen, C.G., (2013). Crop insurance fraud and misrepresentations: Contemporary issues and potential remedies. SSRN. Electron. J., 675-707 (33 Pages).

Mouret, F.; Albughdadi, M.; Duthoit, S.; Kouamé, D.; Rieu, G.; Tourneret, J.Y., (2021). Outlier detection at the parcel-level in wheat and rapeseed crops using multispectral and sar time series. J. Remote Sens., 13(5): 1-25 (25 Pages).

Müller, K.R.; Mika, S.; Tsuda, K.; Schölkopf, K., (2018). An introduction to kernel-based learning algorithms. In handbook of neural network signal processing., 1-4 (4 Pages).

Nassif, A.B.; Talib, M.A.; Nasir, Q.; Dakalbab, F.M., (2021). Machine learning for anomaly detection: A systematic review. IEEE Access., 9(1): 78658-78700 (43 Pages).

Ngai, E.W.T.; Hu, Y.; Wong, Y.H.; Chen, Y.; Sun, X., (2011). The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature. Decis. Support Syst., 50(3): 559-569 (11 Pages).

Randhawa, K.; Loo, C.K.; Seera, M.; Lim, C.P.; Nandi, A.K., (2018). Credit card fraud detection using adaboost and majority voting. IEEE Access., 6(1): 14277-14284 (8 Pages).

Rejesus, R.M.; Little, B.B.; Lovell, A.C., (2004). Using data mining to detect crop insurance fraud: Is there a role for social scientists?. J. Financ. Crime., 12(1): 24-32 (9 Pages).

Roholamin, M.; Paygozar, H.; Khalili Dermani, M.K., (2019). An overview of fraud detection methods in the insurance industry. Paper presented at the 3rd national conference on electrical and computer engineering., 1-10 (10 Pages). [In Persian]

Rostami, H.; MonazamiTabar, S., (2021). Insurance fraud in the compulsory insurance law (2016) (by looking at the american legal system). Iran. J. Insur. Res., 10(3): 155-186 (32 Pages). [In Persian]

Ruff, L.; Kauffmann, J.R.; Vandermeulen, R.A.; Montavon, G.; Samek, W.; Kloft, M.; Dietterich, D.G.; Müller, K.R., (2021). A unifying review of deep and shallow anomaly detection. Proc. IEEE., 109(5): 756-795 (40 Pages).

Sahni, S.; Mittal, A.; Kidwai, F.; Tiwari, A.; Khandelwal, K., (2020). Insurance fraud identification using computer vision and IOT: A study of field fires. Procedia. Comput. Sci., 173(1): 56-63 (8 Pages).

Schölkopf, B.; Platt, J.C.; Shawe-Taylor, J.; Smola, A.J.; Williamson, R.C., (2001). Estimating the support of a high-dimensional distribution. Neural Comput., 13(7): 1443-1471 (29 Pages).

Schölkopf, B.; Williamson, R.C.; Smola, A.; Shawe-Taylor, J.; Platt, J., (1999). Support vector method for novelty detection. Adv. Neural. Inf. Process. Syst., 582-588 (7 Pages).

Soltani Halvaiee, N.; Akbari, M.K., (2014). A novel model for credit card fraud detection using artificial immune systems. Appl. Soft Comput., 24(1): 40-49 (10 Pages).

Verma, A.; Taneja, A.; Arora, A., (2017). Fraud detection and frequent pattern matching in insurance claims using data mining techniques. Paper presented at the 2017 10th international conference on contemporary computing (IC3)., 1-7 (7 Pages).

Zhao, Y.; Hu, X.; Cheng, C.; Wang, C.; Wan, C.; Wang, W.; Yang, J.; Bai, H.; Li, Z.; Liao, C.; Wang, Y.; Qiao, Z.; Sun, J.; Akoglu, L., (2021). Suod: accelerating large-scale unsupervised heterogeneous outlier detection. Proc. Mach. Learn. Syst., 463-478 (16 Pages).

Zhao, Y.; Nasrullah, Z.; Hryniewicki, M.K.; Li, Z., (2019). LSCP: Locally selective combination in parallel outlier ensembles. Proceedings of the 2019 SIAM international conference on data mining (SDM)., 585-593 (9 Pages).

Zhao, Y.; Nasrullah, Z.; Li, Z., (2019). Pyod: A Python toolbox for scalable outlier detection. J. Mach. Learn. Res., 1-7 (7 Pages).

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Iranian Journal of Insurance Research

Presenting a hybrid model for identifying claims of suspicious damages in agricultural insurance

References

References

Aggarwal, C.C., (2013). Outlier ensembles: Position paper. ACM SIGKDD explorations newsletter., 14(2): 49-58 (10 Pages).

Aggarwal, C.C.; Sahte, S., (2017). Outlier ensembles: An introduction. Springer.

The World Bank (2022). Agriculture & rural development.

Barbado, A.; Corcho, Ó.; Benjamins, R., (2022). Rule extraction in unsupervised anomaly detection for model explainability: Application to OneClass SVM. Expert Syst. Appl., 189(1): 1-20 (20 Pages).

Bauder, R.; Da Rosa, R.; Khoshgoftaar, T., (2018). Identifying medicare provider fraud with unsupervised machine learning. In 2018 IEEE international conference on information reuse and integration (IRI)., 285-292 (8 Pages).

Bose, I.; Mahapatra, R.K., (2001). Business data mining —A machine learning perspective. Inf. Manage., 39(3): 211-225 (15 Pages).

Bouazza, I.; Ameur, E.B.; Ameur, F., (2019). Datamining for fraud detecting, state of the art. Adv. Intell. Syst. Sustainable Dev., 205-219 (15 Pages).

Brownlee, J., (2020). Data preparation for machine learning.

Chandola, V.; Banerjee, A.; Kumar, V., (2009). Anomaly detection: A survey. ACM Comput. Surv., 41(3): 1-58 (58 Pages).

Clayton, C., (2022). FBI touts work on crop insurance fraud case involving kentucky tobacco farmers. Insur. Agents.

Cole, S.A.; Xiong, W., (2017). Agricultural insurance and economic development. Annu. Rev. Econ., 9(1): 235-262 (28 Pages).

Bertoni, D., (2006). Crop insurance more needs to be done to reduce program’s vulnerability to fraud, waste, and abuse. U.S. Gov. Account. Office., 1-18 (18 Pages).

Das, S.; Wong, W.K.; Dietterich, T.; Fern, A.; Emmott, A., (2016). Incorporating expert feedback into active anomaly discovery. In 2016 IEEE 16th international conference on data mining (ICDM)., 853-858 (6 Pages).

Ertekin, S.; Huang, J.; Bottou, L.; Giles, L., (2007). Learning on the border: Active learning in imbalanced data classification. In Proceedings of the 16th ACM conference on conference on information and knowledge management., 127-136 (10 Pages).

Gill, K.M.; Woolley, A.; Gill, M., (2005). Insurance fraud: The business as a victim?. Crime., 73-82 (10 Pages).

Gill, W., (2009). Fighting fraud with advanced analytics. Can. Underwriter. Bus. Inf. Group., 76(9): 28-32 (5 Pages).

Goodarzi, A.; Janatbabaei, S., (2017). Evaluation of Three Data Mining Algorithms (Decision Tree, Naive Bayes, Logistic Regression) in Auto Insurance Fraud Detection. Insur. Res., 1(2): 61-80 (19 Pages). [In Persian]

Letters to Editor

Send comment about this article

Volume 12, Issue 1 - Serial Number 43
February 2023
Pages 63-78

References

The World Bank (2022). Agriculture & rural development.

Letters to Editor

Send comment about this article

Volume 12, Issue 1 - Serial Number 43February 2023Pages 63-78

Volume 12, Issue 1 - Serial Number 43
February 2023
Pages 63-78