Explainable Fraud Detection Hybrid Cloud-Native GenAI for Insurance
DOI:
https://doi.org/10.5281/zenodo.22172787Keywords:
Hybrid Cloud Native Architecture, Insurance Fraud Detection, Explainable Artificial Intelligence, XAI For Fraud Models, Machine Learning Life Cycle Management, MLOps And AIOps Integration, Telecommunications Data Analytics, Proprietary Enterprise Data, Supervised Classification Models, Fraud Risk Scoring, Model Explainability And Transparency, Regulatory Compliance In AI, Class Imbalance Calibration, Business Decision Support Systems, Hybrid Cloud Deployment Models, Operational Fraud Monitoring, AI Governance And Trust, Generative AI For Explainability, Enterprise Scale ML Systems, Risk Mitigation Frameworks.Abstract
Using hybrid cloud-native architecture, machine learning (ML) and AIOps technologies, this study addresses the explainability challenges of insurance fraud identification models built on proprietary telecommunications data. Internal and external enterprise data, together with historical fraud incident records, power supervised classification models for fraud detection. Explainable Artificial Intelligence (XAI) technologies, which shed light on model decision-making processes and outcomes, are essential for business acceptance, regulatory compliance, and alignment with risk-mitigation objectives. The research thus fills a critical gap in the literature, encouraging organizations to move beyond accuracy-centric goals and embrace a more comprehensive view.
The findings reveal that combining data from telecommunication systems with historical fraud event data leads to more accurate models and highlights the importance of class-imbalance calibration. A realistic simulation involving a high-tech anti-fraud operation showcases the potential of a hybrid cloud-native architecture with MLOps capabilities for supporting the whole ML life cycle. With appropriate scrutiny and evaluation, these models can serve business decision-making and be deployed within AIOps procedures built on hybrid cloud architectures. Looking ahead, the investigation points to fruitful future work in two areas: enhancing the explainable capabilities of fraud models through generative AI and establishing new methodological standards for the deployment of explainable fraud detection models within hybrid cloud environments.
References
1. Ali, A., Abd Razak, S., Othman, S. H., Eisa, T. A. E., Al-Dhaqm, A., Nasser, M., Elhassan, T., Elshafie, H., & Saif, A. (2022). Financial fraud detection based on machine learning: A systematic literature review. Applied Sciences, 12(19), 9637.
2. Aslam, F., Hunjra, A. I., Ftiti, Z., Louhichi, W., & Shams, T. (2022). Insurance fraud detection: Evidence from artificial intelligence and machine learning. Research in International Business and Finance, 62, 101744.
3. Hilal, W., Gadsden, S. A., & Yawney, J. (2022). Financial fraud: A review of anomaly detection techniques and recent advances. Expert Systems with Applications, 193, 116429.
4. Al-Hashedi, K. G., & Magalingam, P. (2021). Financial fraud detection applying data mining techniques: A comprehensive review from 2009 to 2019. Computer Science Review, 40, 100402.
5. Ali, A., Razak, S. A., Othman, S. H., Eisa, T. A. E., Al-Dhaqm, A., Nasser, M., Elhassan, T., Elshafie, H., & Saif, A. (2022). Financial fraud detection based on machine learning: A systematic literature review. Applied Sciences, 12(19), 9637.
6. West, J., & Bhattacharya, M. (2022). Intelligent financial fraud detection practices: An investigation using deep learning and explainable artificial intelligence. Journal of Financial Crime, 29(2), 509–526.
7. Pandiri, L., Paleti, S., Kaulwar, P. K., Malempati, M., & Singireddy, J. (2023). Transforming financial and insurance ecosystems through intelligent automation, secure digital infrastructure, and advanced risk management strategies. Educational Administration: Theory and Practice, 29(4), 4777-4793.
8. Carcillo, F., Dal Pozzolo, A., Le Borgne, Y.-A., Caelen, O., Mazzer, Y., & Bontempi, G. (2021). Scarff: A scalable framework for streaming fraud detection. Information Sciences, 557, 1–19.
9. Davuluri, P. S. L. (2023). AI-Augmented Sanctions Screening: Enhancing Accuracy and Latency in Real Time Compliance Systems. AI-Augmented Sanctions Screening: Enhancing Accuracy and Latency in Real Time Compliance Systems (December 15, 2023).
10. Carcillo, F., Le Borgne, Y.-A., Caelen, O., & Bontempi, G. (2021). Streaming active learning strategies for real-life credit card fraud detection. Data Mining and Knowledge Discovery, 35, 124–160.
11. Van Vlasselaer, V., Akoglu, L., Eliassi-Rad, T., & Baesens, B. (2021). Network-based fraud detection with graph neural networks. IEEE Transactions on Knowledge and Data Engineering, 33(7), 2847–2860.
12. Singireddy, S. (2024). The Integration of AI and Machine Learning in Transforming Underwriting and Risk Assessment Across Personal and Commercial Insurance Lines. Journal of Computational Analy-sis and Applications (JoCAAA), 33(08), 3966-3991.
13. Khandani, A. E., Kim, A. J., & Lo, A. W. (2021). Consumer credit-risk models via machine-learning algorithms. Journal of Banking & Finance, 135, 106–123.
14. Annapareddy, V. N. (2024). Leveraging Artificial Intelligence, Machine Learning, and Cloud-Based IT Integrations to Optimize Solar Power Systems and Renewable Energy Management. Machine Learning, and Cloud-Based IT Integrations to Optimize Solar Power Systems and Renewable Energy Management (December 06, 2024).
15. Lebichot, B., Le Borgne, Y.-A., He-Guelton, L., Oblé, F., & Bontempi, G. (2021). Deep-learning domain adaptation techniques for credit-card fraud detection. Neural Information Processing Systems, 34, 121–132.
16. Sriram, H. K., Challa, S. R., Challa, K., & ADUSUPALLI, B. (2024). Strategic Financial Growth: Strengthening Investment Management. Secure Transactions, and Risk Protection in the Digital Era. Secure Transactions, and Risk Protection in the Digital Era (November 10, 2024).
17. Jurgovsky, J., Granitzer, M., Ziegler, K., Calabretto, S., Portier, P.-E., He-Guelton, L., & Caelen, O. (2021). Sequence classification for credit-card fraud detection. Expert Systems with Applications, 100, 234–245.
18. Zhao, Q., Zhang, Y., & Chen, X. (2021). Machine learning-based fraud detection in insurance claims: A comparative study. International Journal of Information Management, 58, 102–117.
19. Sheelam, G. K. (2024). AI-driven spectrum management: Using machine learning and agentic intelligence for dynamic wireless optimization. European Advanced Journal for Emerging Technologies (EAJET), 2(1), 3050-9742.
20. Viaene, S., Van den Borne, J., & Dedene, G. (2021). Neural network architectures for insurance fraud detection and risk assessment. Expert Systems with Applications, 174, 114–128.
21. Yang, W., Zheng, Y., Li, Y., & Wang, J. (2021). A deep learning framework for insurance claim fraud detection. Applied Soft Computing, 112, 107–121.
22. Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E., Brynjolfsson, E., Buch, S., Card, D., Castellon, R., Chatterji, N., Chen, A., Creel, K., Davis, J. Q., Demszky, D., ... Liang, P. (2021). On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258.
23. Srinivas Kalisetty, D. A. S. (2024). Leveraging Artificial Intelligence and Machine Learning for Predictive Bid Analysis in Supply Chain Management: A Data-Driven Approach to Optimize Procurement Strategies.
24. Jakubik, J., Vössing, M., Kühl, N., Walk, J., & Satzger, G. (2024). Data-centric artificial intelligence. Business & Information Systems Engineering, 66, 507–515.
25. Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C. L., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., Schulman, J., Hilton, J., Kelton, F., Miller, L., Simens, M., Askell, A., Welinder, P., Christiano, P. F., Leike, J., & Lowe, R. (2022). Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems, 35, 27730–27744.
26. Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E., Le, Q. V., & Zhou, D. (2022). Chain-of-thought prompting elicits reasoning in large language models. Advances in Neural Information Processing Systems, 35, 24824–24837.
27. Ikudabo, A. O., & Kumar, P. (2024). AI-driven risk assessment and management in banking: balancing innovation and security. International Journal of Research Publication and Reviews, 5(10), 3573-88.
28. Hu, E. J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., & Chen, W. (2022). LoRA: Low-rank adaptation of large language models. International Conference on Learning Representations.
29. Min, B., Ross, H., Sulem, E., Veyseh, A. P. B., Nguyen, T. H., Sainz, O., Agirre, E., Heintz, I., & Roth, D. (2023). Recent advances in natural language processing via large pre-trained language models: A survey. ACM Computing Surveys, 56(2), 1–40.
30. Ramanakar Reddy Danda, Z. Y., Mandala, G., & Maguluri, K. K. (2024). Smart Medicine: The Role of Artificial Intelligence and Machine Learning in Next-Generation Healthcare Innovation.
31. Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., Rodriguez, A., Joulin, A., Grave, E., & Lample, G. (2023). LLaMA: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971.
32. OpenAI. (2023). GPT-4 technical report. arXiv preprint arXiv:2303.08774.
33. Kummari, D. N. (2022). IoT-enabled additive manufacturing: Improving prototyping speed and customization in the automotive sector. Migration Letters, 19(S8), 2084-2104.
34. Bubeck, S., Chandrasekaran, V., Eldan, R., Gehrke, J., Horvitz, E., Kamar, E., Lee, P., Lee, Y. T., Li, Y., Lundberg, S., Nori, H., Palangi, H., Ribeiro, M. T., & Zhang, Y. (2023). Sparks of artificial general intelligence: Early experiments with GPT-4. arXiv preprint arXiv:2303.12712.
35. Zhao, W. X., Zhou, K., Li, J., Tang, T., Wang, X., Hou, Y., Min, Y., Zhang, B., Zhang, J., Dong, Z., Du, Y., Yang, C., Chen, Y., Chen, Z., Jiang, J., Ren, R., Li, Y., Tang, X., Liu, Z., ... Wen, J.-R. (2023). A survey of large language models. arXiv preprint arXiv:2303.18223.
36. Kolla, S., Meda, R., Balleda, L., & Thimmapuram, C. R. (2024). The utility value of ROX index and modified ROX index in determining the efficiency of HFNC in children admitted with respiratory distress. International Journal of Contemporary Pediatrics, 11(6), 775.
37. Gao, Y., Xiong, Y., Gao, X., Jia, K., Pan, J., Bi, Y., Dai, Y., Sun, J., Wang, H., & Wang, H. (2023). Retrieval-augmented generation for large language models: A survey. arXiv preprint arXiv:2312.10997.
38. Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W.-t., Rocktäschel, T., Riedel, S., & Kiela, D. (2021). Retrieval-augmented generation for knowledge-intensive NLP tasks. Advances in Neural Information Processing Systems, 33, 9459–9474.
39. Kreuzberger, D., Kühl, N., & Hirschl, S. (2023). Machine learning operations (MLOps): Overview, definition, and architecture. IEEE Access, 11, 31866–31879.
40. Koppolu, H. K. R. (2024). The impact of data engineering on service quality in 5G-enabled cable and media networks. European Advanced Journal for Science & Engineering (EAJSE), 1(1).
41. Jakubik, J., Vössing, M., Kühl, N., Walk, J., & Satzger, G. (2022). Data-centric artificial intelligence. arXiv preprint arXiv:2212.11854.
42. Hancock, J. T., Bauder, R. A., Wang, H., & Khoshgoftaar, T. M. (2023). Explainable machine learning models for Medicare fraud detection. Journal of Big Data, 10, 154.
43. Debener, J., Baesens, B., & Verbeke, W. (2023). Detecting insurance fraud using supervised and unsupervised machine learning. Journal of Risk and Insurance, 90(4), 1059–1090.
44. Singireddy, J. (2024). AI-driven payroll systems: Ensuring compliance and reducing human error. American Data Science Journal for Advanced Computations (ADSJAC) ISSN, 3067-4166.
45. Meske, C., Bunde, E., Schneider, J., & Gersch, M. (2022). Explainable artificial intelligence: Objectives, stakeholders, and future research opportunities. Information Systems Management, 39(1), 53–63.
46. Füller, J., Hutter, K., & Krüger, C. (2021). Explainable artificial intelligence in business and management: A systematic literature review. Business & Information Systems Engineering, 63, 1–15.
47. Explainable Artificial Intelligence (XAI) in Insurance. (2022). Risks, 10(12), 230.
48. Khan, Z., Olivia, D., & Shetty, S. (2024). A machine learning and explainable artificial intelligence approach for insurance fraud classification. Journal of Information, Technology and Policy.
49. Wilson, A., Xu, K., Zhang, Z., & Qiao, Y. (2024). The interpretable artificial neural network in vehicle insurance claim fraud detection based on Shapley additive explanations. Journal of Information, Technology and Policy, 2(1), 1–12.
50. Mudigonda, S. S., Baruah, P. K., Kandala, P. K., Gupta, R. Y., Hegde, S. N., & Chebrolu, S. (2024). Using interpretable machine learning methods: An application to health insurance fraud detection. Society of Actuaries.
51. Yadav, V. (2024). Predictive Analytics for Preventive Medicine: Analyzing how Predictive Analytics is Utilized for Forecasting Patient Health Trends and Preventive Disease. Progress in Medical Sciences. PMS-1126. Prog Med Sci, 8(4).
52. Automobile insurance fraud detection using data mining: A systematic literature review. (2024). Intelligent Systems with Applications, 21, 200340.
53. Data misrepresentation detection for insurance underwriting fraud prevention. (2022). Decision Support Systems, 159, 113798.
54. A user-centered explainable artificial intelligence approach for financial fraud detection. (2023). Finance Research Letters, 58, 104309.
55. Ghosh, S., Dutta, S., & Kumar, A. (2022). Explainable artificial intelligence for financial fraud detection: A systematic review and research agenda. Journal of Risk and Financial Management, 15(11), 1–22.
56. Ghosh, S., Majumder, A., & Singh, P. (2023). Deep learning and explainable artificial intelligence for financial fraud analytics. Expert Systems with Applications, 213, 118–130.
57. Huang, Y., Li, C., & Wang, Z. (2023). Graph neural networks for financial fraud detection: A survey. IEEE Access, 11, 104321–104340.
58. Ribeiro, M. T., Singh, S., & Guestrin, C. (2022). Model-agnostic explainability for high-stakes machine learning applications. ACM Computing Surveys, 55(8), 1–36.
59. Wilson, A., Xu, K., Zhang, Z., & Qiao, Y. (2024). Explainable deep learning approaches for intelligent insurance fraud analytics. Journal of Information, Technology and Policy, 2(1), 13–25.
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