International Journal of Computer and Information Technology

Open Access Open Access  Restricted Access Subscription Access

Fraud Detection in Motor Insurance Claims Using Supervised Learning Techniques: A Review


Affiliations
1 Department of Computer Science, Dedan Kimathi University of Technology, Nyeri, Kenya
 

Fraudulent claims have been a big drawback in motor insurance despite the insurance industry having vast amounts of motor claims data. Analyzing this data can lead to a more efficient way of detecting reported fraudulent claims. The challenge is how to extract insightful information and knowledge from this data and use it to model a fraud detection system. Due to constant evolution and dynamic nature of fraudsters, some approaches utilized by insurance firms, such as impromptu audits, whistle-blowing, staff rotation have become infeasible. Machine learning techniques can aid in fraud detection by training a prediction model using historical data. The performance of the models is affected by class imbalance and the determination of the most relevant features that might lead to fraud detection from data. In this paper we examine various fraud detection techniques and compare their performance efficiency. We then give a summary of techniques’ strengths and weaknesses in identifying claims as either fraudulent or non-fraudulent, and finally propose a fraud detection framework of an ensemble model that is trained on dataset balanced using SMOTE and with relevant features only. This proposed approach would improve performance and reduce false positives.

Keywords

Insurance, Fraud, Class Imbalance, SMOTE, Feature Selection, Ensemble Learning
User
Notifications
Font Size

  • B. Bart, H. Sebastiaan, and C. Oppner, “Verdonck.(2021),” Data engineering for fraud detection. Decision Support Systems Jurnal. https://doi. org/10.1016/j. dss, 2021.

  • L. Goleiji and M. J. Tarokh, “Fraud detection in the insurance using decision tree, naive bayesian and support vector machine data mining algorithms (case study-automobile’s body insurance),” 2016.

  • R. Roy and K. T. George, “Detecting insurance claims fraud using machine learning techniques,” in 2017 international conference on circuit, power and computing technologies (ICCPCT). IEEE, 2017, pp. 1–6.

  • A. Association of Kenya Insurers, “Insurance Industry Report 2021,” Tech. Rep., 2021.

  • N. Remli, F. Salleh, and J. Arifin, “Motor insurance fraudulent claims: An overview reconnaissance,” International Journal of Business, Economics and Law, vol. 25, 2021.

  • S. Subudhi and S. Panigrahi, “Detection of automobile insurance fraud using feature selection and data mining techniques,” International Journal of Rough Sets and Data Analysis (IJRSDA), vol. 5, no. 3, pp. 1–20, 2018.

  • B. Itri, Y. Mohamed, Q. Mohammed, and B. Omar, “Performance comparative study of machine learning algorithms for automobile insurance fraud detection,” in 2019 Third International Conference on Intelligent Computing in Data Sciences (ICDS). IEEE, 2019, pp. 1–4.

  • G. Kowshalya and M. Nandhini, “Predicting fraudulent claims in automobile insurance,” in 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT). IEEE, 2018, pp. 1338–1343.

  • M. A. Rashid, A. Al-Mamun, H. Roudaki, and Q. R. Yasser, “An overview of corporate fraud and its prevention approach,” Australasian Accounting Business & Finance Journal, vol. 16, no. 1, pp. 101–118, 2022.

  • M. H. AYBOGA and F. Ganji, “Detecting fraud in insurance companies and solutions to fight it using coverage data in the covid 19 pandemic,” PalArch’s Journal of Archaeology of Egypt/Egyptology, vol. 18, no. 15, pp. 392–407, 2021.

  • U. Rani, O. L. Pramudyastuti, and A. P. Nugraheni, “Disclosing the practice of whistleblowing system in indonesiaaˆC™ s public listed companies,” INOVASI, vol. 18, pp. 79–87, 2022.

  • N. Dhieb, H. Ghazzai, H. Besbes, and Y. Massoud, “A secure ai-driven architecture for automated insurance systems: Fraud detection and risk measurement,” IEEE Access, vol. 8, pp. 58546–58558, 2020.

  • K. G. Al-Hashedi and P. Magalingam, “Financial fraud detection applying data mining techniques: A comprehensive review from 2009 to 2019,” Computer Science Review, vol. 40, p. 100402, 2021.

  • M. Guillen, J. P. Nielsen, and A. M. Perez-Mar´ ´ın, “Near-miss telematics in motor insurance,” Journal of Risk and Insurance, vol. 88, no. 3, pp. 569–589, 2021.

  • L. Rukhsar, W. H. Bangyal, K. Nisar, and S. Nisar,

  • “Prediction of insurance fraud detection using machine learning algorithms,” Mehran University Research Journal Of Engineering Technology, vol. 41, no. 1, p. 33–40, 2022. [Online]. Available:

  • https://search.informit.org/doi/10.3316/informit.263147785515876

  • A. Kini, R. Chelluru, K. Naik, D. Naik, S. Aswale, and P. Shetgaonkar, “Automobile insurance fraud detection: An overview,” in 2022 3rd International Conference on Intelligent Engineering and Management (ICIEM), 2022, pp. 7–12.

  • C. Eckert and K. Osterrieder, “How digitalization affects insurance companies: overview and use cases of digital technologies,” Zeitschrift fur die gesamte Versicherungswissenschaft¨ , vol. 109, 10 2020.

  • H. L. Sithic and T. Balasubramanian, “Survey of insurance fraud detection using data mining techniques,” arXiv preprint arXiv:1309.0806, 2013.

  • A. Abdallah, M. A. Maarof, and A. Zainal, “Fraud detection system: A survey,” Journal of Network and Computer Applications, vol. 68, pp. 90–113, 2016.

  • N. J. Morley, L. J. Ball, and T. C. Ormerod, “How the detection of insurance fraud succeeds and fails,” Psychology, Crime & Law, vol. 12, no. 2, pp. 163–180, 2006.

  • O. Tajudeen and R. Abdur, “Control of insurance fraud in nigeria: an exploratory study (case study),” J. Financ. Crime, vol. 16, no. 4, pp. 418–435, 2009.

  • E. J. Efiong, I. O. Inyang, and U. Joshua, “Effectiveness of the mechanisms of fraud prevention and detection in nigeria,” Advances in Social Sciences Research Journal, vol. 3, no. 3, 2016.

  • R. Othman, N. A. Aris, A. Mardziyah, N. Zainan, and N. M. Amin, “Fraud detection and prevention methods in the malaysian public sector: Accountants’ and internal auditors’ perceptions,” Procedia Economics and Finance, vol. 28, pp. 59–67, 2015.

  • S. W. Mwangi and J. Ndegwa, “The influence of fraud risk management on fraud occurrence in kenyan listed companies,” International Journal of Finance amp; Banking Studies (21474486), vol. 9, no. 4, p. 147–160, 2020. [Online]. Available: https://www.ssbfnet.com/ojs/index.php/ijfbs/article/view/943

  • J. O. Otieno, “Insurance stakeholders’ perceptions on effectiveness and usage of fraud detection and prevention techniques in motor insurance sector,” Ph.D. dissertation, Strathmore University, 2018.

  • W. S. Albrecht, C. Albrecht, C. Albrecht, and M. Zimbelman, “Fraud examination 2e,” Baskı. Thomson South-Western, 2006.

  • S. Viaene, M. Ayuso, M. Guillen, D. Van Gheel, and G. Dedene,

  • “Strategies for detecting fraudulent claims in the automobile insurance industry,” European Journal of Operational Research, vol. 176, no. 1, pp. 565–583, 2007.

  • Z. A. Soomro, J. Ahmed, M. H. Shah, and K. Khoumbati, “Investigating identity fraud management practices in e-tail sector: a systematic review,” Journal of Enterprise Information Management, 2019.

  • E. Fernando, “Machine learning approaches on motor insurance fraud detection,” Ph.D. dissertation, 2022.

  • R. Garcia-Dias, A. Mechelli, W. L. Pinaya, and S. Vieira, “Autoencoders,” Machine Learning: Methods and Applications to Brain Disorders, Academic Press, Cambridge, 2019.

  • P. Singh, S. P. Singh, and D. S. Singh, “An introduction and review on machine learning applications in medicine and healthcare,” 2019 IEEE Conference on Information and Communication Technology, CICT 2019, 2019.

  • Z. Ge, Z. Song, S. X. Ding, and B. Huang, “Data Mining and Analytics in the Process Industry: The Role of Machine Learning,” IEEE Access, vol. 5, pp. 20590–20616, 2017.

  • M. N. Ashtiani and B. Raahemi, “Intelligent Fraud Detection in Financial Statements using Machine Learning and Data Mining: A Systematic Literature Review,” IEEE Access, vol. 10, pp. 72504–72525, 2021.

  • J. Liu, “From statistics to data mining: A brief review,” Proceedings 2020 International Conference on Computing and Data Science, CDS 2020, vol. 7, pp. 343–346, 2020.

  • D. Sarkar, R. Bali, and T. Sharma, Practical Machine Learning with Python A Problem-Solver’s Guide to Building Real-World Intelligent Systems. Library of Congress Control Number: 2017963290, 2018.

  • N. K. Trivedi, S. Simaiya, U. K. Lilhore, and S. K. Sharma, “An efficient credit card fraud detection model based on machine learning methods,” International Journal of Advanced Science and Technology, vol. 29, no. 5, pp. 3414–3424, 2020.

  • N. Fatima, L. Liu, S. Hong, and H. Ahmed, “Prediction of breast cancer, comparative review of machine learning techniques, and their analysis,” IEEE Access, vol. 8, pp. 150360–150376, 2020.

  • R. Hegde, G. Anusha, S. Madival, H. Sowjanya, and U. Sushma, “A review on data mining and machine learning methods for student scholarship prediction,” in 2021 5th International Conference on Computing Methodologies and Communication (ICCMC). IEEE, 2021, pp. 923– 927.

  • I. M. N. Prasasti, A. Dhini, and E. Laoh, “Automobile insurance fraud detection using supervised classifiers,” in 2020 International Workshop on Big Data and Information Security (IWBIS). IEEE, 2020, pp. 47–52.

  • J. T. Hancock and T. M. Khoshgoftaar, “Gradient boosted decision tree algorithms for medicare fraud detection,” SN Computer Science, vol. 2, no. 4, p. 268, 2021.

  • N. Dhieb, H. Ghazzai, H. Besbes, and Y. Massoud, “Extreme gradient boosting machine learning algorithm for safe auto insurance operations,” in 2019 IEEE international conference on vehicular electronics and safety (ICVES). IEEE, 2019, pp. 1–5.

  • D. X. Cho, D. N. Phong, and N. Duy Phuong, “A new approach for detecting credit card fraud transaction,” International Journal of Nonlinear Analysis and Applications, 2023.

  • J. R. D. Kho and L. A. Vea, “Credit card fraud detection based on transaction behavior,” in TENCON 2017-2017 IEEE Region 10 Conference. IEEE, 2017, pp. 1880–884.

  • J. V. Devi and K. Kavitha, “Fraud detection in credit card transactions by using classification algorithms,” in 2017 International Conference on Current Trends in Computer, Electrical, Electronics and Communication (CTCEEC). IEEE, 2017, pp. 125–131.

  • S. Subudhi and S. Panigrahi, “Effect of class imbalanceness in detecting automobile insurance fraud,” in 2018 2nd International Conference on Data Science and Business Analytics (ICDSBA). IEEE, 2018, pp. 528– 531.

  • W. Hilal, S. A. Gadsden, and J. Yawney, “Financial fraud: A review of anomaly detection techniques and recent advances,” Expert Systems with Applications, vol. 193, p. 116429, 2022.

  • A. B. Nassif, M. A. Talib, Q. Nasir, and F. M. Dakalbab, “Machine learning for anomaly detection: A systematic review,” Ieee Access, vol. 9, pp. 78658–78700, 2021.

  • G. G. Sundarkumar, V. Ravi, and V. Siddeshwar, “One-class support vector machine based undersampling: Application to churn prediction and insurance fraud detection,” in 2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC). IEEE, 2015, pp. 1–7.

  • N. Rtayli and N. Enneya, “Selection features and support vector machine for credit card risk identification,” Procedia Manufacturing, vol. 46, pp. 941–948, 2020.

  • A. Verma, A. Taneja, and A. Arora, “Fraud detection and frequent pattern matching in insurance claims using data mining techniques,” in 2017 tenth international conference on contemporary computing (IC3). IEEE, 2017, pp. 1–7.

  • T. Badriyah, L. Rahmaniah, and I. Syarif, “Nearest neighbour and statistics method based for detecting fraud in auto insurance,” in 2018 International Conference on Applied Engineering (ICAE). IEEE, 2018, pp. 1–5.

  • A. Urunkar, A. Khot, R. Bhat, and N. Mudegol, “Fraud detection and analysis for insurance claim using machine learning,” in 2022 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems (SPICES), vol. 1. IEEE, 2022, pp. 406–411.

  • M. Madhurya, H. Gururaj, B. Soundarya, K. Vidyashree, and A. Rajendra, “Exploratory analysis of credit card fraud detection using machine learning techniques,” Global Transitions Proceedings, vol. 3, no. 1, pp. 31–37, 2022.

  • V. N. Dornadula and S. Geetha, “Credit card fraud detection using machine learning algorithms,” Procedia computer science, vol. 165, pp. 631–641, 2019.

  • S. Bagga, A. Goyal, N. Gupta, and A. Goyal, “Credit card fraud detection using pipeling and ensemble learning,” Procedia Computer Science, vol. 173, pp. 104–112, 2020.


Abstract Views: 103

PDF Views: 50




  • Fraud Detection in Motor Insurance Claims Using Supervised Learning Techniques: A Review

Abstract Views: 103  |  PDF Views: 50

Authors

David Gichohi Maina
Department of Computer Science, Dedan Kimathi University of Technology, Nyeri, Kenya
Juliet Chebet Moso
Department of Computer Science, Dedan Kimathi University of Technology, Nyeri, Kenya
Patrick Kinyua Gikunda
Department of Computer Science, Dedan Kimathi University of Technology, Nyeri, Kenya

Abstract


Fraudulent claims have been a big drawback in motor insurance despite the insurance industry having vast amounts of motor claims data. Analyzing this data can lead to a more efficient way of detecting reported fraudulent claims. The challenge is how to extract insightful information and knowledge from this data and use it to model a fraud detection system. Due to constant evolution and dynamic nature of fraudsters, some approaches utilized by insurance firms, such as impromptu audits, whistle-blowing, staff rotation have become infeasible. Machine learning techniques can aid in fraud detection by training a prediction model using historical data. The performance of the models is affected by class imbalance and the determination of the most relevant features that might lead to fraud detection from data. In this paper we examine various fraud detection techniques and compare their performance efficiency. We then give a summary of techniques’ strengths and weaknesses in identifying claims as either fraudulent or non-fraudulent, and finally propose a fraud detection framework of an ensemble model that is trained on dataset balanced using SMOTE and with relevant features only. This proposed approach would improve performance and reduce false positives.

Keywords


Insurance, Fraud, Class Imbalance, SMOTE, Feature Selection, Ensemble Learning

References