Generative AI-Driven Real-Time Insurance Fraud Detection in Cloud Environments
DOI:
https://doi.org/10.5281/ks0mvj84Keywords:
Generative Artificial Intelligence (GenAI),Real-Time Fraud Detection,Insurance Analytics,Cloud-Based Machine Learning,Deep Learning Architectures,Anomaly Detection Systems,Synthetic Data Generation,Hybrid AI Models,Explainable AI (XAI),Streaming Data Processing,Autoencoder Networks,Model Drift Detection,Scalable AI Infrastructure,Risk Scoring Algorithms,Cyber-Fraud Prevention Systems.Abstract
Rapid advances in Generative AI (GenAI) in recent months open new possibilities for enhancing real-time, online, and incrementally adaptive Machine Learning (ML) models. In this paper, the GenAI paradigm is examined from a cloud feature streaming perspective, enabling a combination of feature engineering and ML model training. Modest inference latency challenges in Online Learning pipeline architectures are resolved using latency-optimized inference pipelines. These concepts are applied to the area of Fraud Detection in Insurance, a phase 1 use case in the ACGA-Cloud Digital Lab, resulting in two contributions: (i) real-time ML models with a 30×39 meilleure separation for Insurance Fraud Detection; and (ii) the application of GANs for transductive domain adaptation and feature augmentation. These contributions are deployed and made available as enhanceML use cases.
Generative AI (GenAI) has recently seen rapid advances, culminating in the release of large natural-language models with unprecedented capabilities. While reflexive initial reactions response in Fine-Tuning, Prompt Engineering, and even Code Generation, the true power of GenAI is revealed with the correct application to traditional modelling domains. In cloud-based ML model training architectures such as Feature Streaming Training and Online Learning, there is plenty of feature engineering required for structural domain knowledge, and at least feature radiating into an ML Model-Space-Super-Space location through within a Generative-Adversarial-Network Combination. The Online Learning pipe is inherently segmented according to subdomain sections of the space, but can be routed with appropriate pipelines to ensure minimal inference latency degradation at these segmentation locations.
References
1. Gomes, C., Jin, Z., & Yang, H. (2021). Insurance fraud detection with unsupervised deep learning. Journal of Risk and Insurance, 88(3), 591–624.
2. Carcillo, F., Le Borgne, Y.-A., Caelen, O., Kessaci, Y., Oblé, F., & Bontempi, G. (2021). Combining unsupervised and supervised learning in credit card fraud detection. Information Sciences, 557, 317–331.
3. Pandugula, C., Ganti, V. K. A. T., & Mallesham, G. (2024). Predictive Modeling in Assessing the Efficacy of Precision Medicine Protocols. EDUCATIONAL ADMINISTRATION: THEORY AND PRACTICE Учредители: Green Publication БИБЛИОМЕТРИЧЕСКИЕ ПОКАЗАТЕЛИ: Входит в РИНЦ: на рассмотрении Цитирований в РИНЦ: 0 Входит в ядро РИНЦ: нет Цитирований из ядра РИНЦ: 0 Рецензии: нет данных Процентиль журнала в рейтинге SI: ТЕМАТИЧЕСКИЕ НАПРАВЛЕНИЯ:.
4. Ileberi, E., Sun, Y., & Wang, Z. (2021). Performance evaluation of machine learning methods for credit card fraud detection using SMOTE and AdaBoost. IEEE Access, 9, 165286–165294.
5. 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.
6. Hassan, H. B., Barakat, S. A., & Sarhan, Q. I. (2021). Survey on serverless computing. Journal of Cloud Computing, 10, 39.
7. Polineni, T. N. S., Kumar, A. S., Maguluri, K. K., Koli, V., Valiki, D., & Ravikanth, S. (2024, November). A Scalable and Robust Framework for Advanced Semi Supervised Learning Supporting Universal Applications. In Proceedings of the 3rd International Conference on Optimization Techniques in the Field of Engineering (ICOFE-2024).
8. Błaszczyński, J., de Almeida Filho, A. T., Matuszyk, A., Szeląg, M., & Słowiński, R. (2021). Auto loan fraud detection using dominance-based rough set approach versus machine learning methods. Expert Systems with Applications, 163, 113740.
9. 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.
10. Singireddy, S., Adusupalli, B., Pamisetty, A., Mashetty, S., & Kaulwar, P. K. (2024). Redefining financial risk strategies: The integration of smart automation, secure access systems, and predictive intelligence in insurance, lending, and asset management. Journal of Artificial Intelligence and Big Data Disciplines, 1(1), 109-124.
11. Abe, K., Kim, S., Nojima, R., Ozawa, S., & Moriai, S. (2022). Privacy-preserving federated learning for detecting fraudulent financial transactions in Japanese banks. Journal of Information Processing, 30, 789–795.
12. 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.
13. Bhowmik, A., Sannigrahi, M., Chowdhury, D., Dwivedi, A. D., & Mukkamala, R. R. (2022). DBNex: Deep belief network and explainable AI based financial fraud detection. In Proceedings of the 2022 IEEE International Conference on Big Data (pp. 3033–3042). IEEE.
14. Shyamala Anto Mary, P., Kalisetty, S., & Mandala, V. M. (2024, November). Advancing IoT Data Forecasting with Deep Learning Framework for Resilience Scalability and Real-World Applications. In Proceedings of the 3rd International Conference on Optimization Techniques in the Field of Engineering (ICOFE-2024).
15. Shafiei, H., Khonsari, A., & Mousavi, P. (2022). Serverless computing: A survey of opportunities, challenges, and applications. ACM Computing Surveys, 54(11s), Article 239, 1–32.
16. Marin, E., Perino, D., & Di Pietro, R. (2022). Serverless computing: A security perspective. Journal of Cloud Computing, 11, 69.
17.
18. Recharla, M. (2024). Antioxidants, Biological Markers, Catalase, Glutathione Peroxidase, Chronic Periodontitis, Saliva, Smokeless tobacco, Smoker. Frontiers in Health Informatics, 13(8), 4999.
19. Li, Z., Guo, L., Cheng, J., Chen, Q., He, B., & Guo, M. (2022). The serverless computing survey: A technical primer for design architecture. ACM Computing Surveys, 54(10s), Article 220, 1–34.
20. Xiao, S., Bai, T., Cui, X., Wu, B., Meng, X., & Wang, B. (2023). A graph-based contrastive learning framework for Medicare insurance fraud detection. Frontiers of Computer Science, 17(2), 172341.
21. Mashetty, S. (2024). Research insights into the intersection of mortgage analytics, community investment, and affordable housing policy. Available at SSRN 5249213.
22. Debener, J., Heinke, V., & Kriebel, J. (2023). Detecting insurance fraud using supervised and unsupervised machine learning. Journal of Risk and Insurance, 90(3), 743–775.
23. Lu, J., Lin, K., Chen, R., et al. (2023). Health insurance fraud detection by using an attributed heterogeneous information network with a hierarchical attention mechanism. BMC Medical Informatics and Decision Making, 23, 62.
24. Afriyie, J. K., Tawiah, K., Pels, W. A., Addai-Henne, S., Dwamena, H. A., Owiredu, E. O., Ayeh, S. A., & Eshun, J. (2023). A supervised machine learning algorithm for detecting and predicting fraud in credit card transactions. Decision Analytics Journal, 6, 100163.
25. Pamisetty, A. (2024). Leveraging Big Data Engineering for Predictive Analytics in Wholesale Product Logistics. Available at SSRN 5231473.
26. Fanai, H., & Abbasimehr, H. (2023). A novel combined approach based on deep autoencoder and deep classifiers for credit card fraud detection. Expert Systems with Applications, 217, 119562.
27. Chen, Z.-Y., & Han, D. (2023). Detecting corporate financial fraud via two-stage mapping in joint temporal and financial feature domain. Expert Systems with Applications, 217, 119559.
28. Yi, Z., Cao, X., Pu, X., Wu, Y., Chen, Z., Khan, A. T., Francis, A., & Li, S. (2023). Fraud detection in capital markets: A novel machine learning approach. Expert Systems with Applications, 231, 120760.
29. Nandan, B. P. (2024). Semiconductor Process Innovation: Leveraging Big Data for Real-Time Decision-Making. Journal of Computational Analysis and Applications (JoCAAA), 33(08), 4038-4053.
30. Zhou, Y., Li, H., Xiao, Z., & Qiu, J. (2023). A user-centered explainable artificial intelligence approach for financial fraud detection. Finance Research Letters, 58, 104309.
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. Dettmers, T., Pagnoni, A., Holtzman, A., & Zettlemoyer, L. (2023). QLoRA: Efficient finetuning of quantized LLMs. Advances in Neural Information Processing Systems, 36.
33. Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., & Cao, Y. (2023). ReAct: Synergizing reasoning and acting in language models. In International Conference on Learning Representations.
34. Mashetty, S. (2024). The role of US patents and trademarks in advancing mortgage financing technologies. European Advanced Journal for Science & Engineering (EAJSE)-p-ISSN, 3050-9696.
35. Asai, A., Wu, Z., Wang, Y., Sil, A., & Hajishirzi, H. (2024). Self-RAG: Learning to retrieve, generate, and critique through self-reflection. In International Conference on Learning Representations.
36. Liu, N. F., Lin, K., Hewitt, J., Paranjape, A., Bevilacqua, M., Petroni, F., & Liang, P. (2024). Lost in the middle: How language models use long contexts. Transactions of the Association for Computational Linguistics, 12, 157–173.
37. OpenAI, Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F. L., Almeida, D., Altenschmidt, J., Altman, S., et al. (2023). GPT-4 technical report. arXiv preprint arXiv:2303.08774.
38. Kreuzberger, D., Kühl, N., & Hirschl, S. (2023). Machine learning operations (MLOps): Overview, definition, and architecture. IEEE Access, 11, 31866–31879.
39. Pamisetty, V. (2024). Transforming taxation systems through predictive analytics and AI-driven compliance monitoring tools. Am Data Sci J Adv Comput, 3, 55-68.
40. Schrijver, G., Sarmah, D. K., & El-hajj, M. (2024). Automobile insurance fraud detection using data mining: A systematic literature review. Intelligent Systems with Applications, 21, 200340.
41. Banulescu-Radu, D., & Yankol-Schalck, M. (2024). Practical guideline to efficiently detect insurance fraud in the era of machine learning: A household insurance case. Journal of Risk and Insurance, 91(4), 867–913.
42. Hong, B., Lu, P., & Yang, Y. (2024). Health insurance fraud detection based on multi-channel heterogeneous graph structure learning. Heliyon, 10(9), e30045.
43. Vorobyev, I. (2024). Fraud risk assessment in car insurance using claims graph features in machine learning. Expert Systems with Applications, 251, 124109.
44. Motie, S., & Raahemi, B. (2024). Financial fraud detection using graph neural networks: A systematic review. Expert Systems with Applications, 240, 122156.
45. Paleti, S. Agentic AI in Financial Decision-Making: Enhancing Customer Risk Profiling. Predictive Loan Approvals, and Automated Treasury Management in Modern Banking.
46. Awosika, T., Shukla, R. M., & Pranggono, B. (2024). Transparency and privacy: The role of explainable AI and federated learning in financial fraud detection. IEEE Access, 12, 64551–64560.
47. Abdul Salam, M., Fouad, K. M., Elbably, D. L., & Elsayed, S. M. (2024). Federated learning model for credit card fraud detection with data balancing techniques. Neural Computing and Applications, 36, 6231–6256.
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