AI's Role in Modern Insurance Claims

Authors

  • Luca Bianchi Author

Keywords:

Artificial Intelligence In Insurance, Automated Claims Processing, Insurance Operations Optimization, Cost Reduction Strategies, Customer Experience Enhancement, Claims Settlement Acceleration, Ethical AI Governance, AI-Driven Decision Support, Small Claims Automation, Operational Efficiency Gains, Customer Satisfaction Management, Competitive Advantage In Insurance, Claims Accuracy And Consistency, Service Quality Improvement, Customer Retention And Loyalty, High-Value Client Management, Digital Insurance Transformation, AI Monitoring And Oversight, Intelligent Claims Workflows, Experience-Driven Insurance Services.

Abstract

Over the past decade, claims processing has emerged as a primary area of interest for insurers exploring how artificial intelligence (AI) can reduce costs or enhance service quality. Several factors motivate this focus. First, processing claims represents a major operational expense—one that, if reduced, is likely to translate into greater profitability. Second, claims processing often significantly influences the customer experience and may even determine clients’ future purchase decisions. Finally, AI technologies tailored for claims processing have demonstrated the ability to deliver faster settlement of smaller claims without sacrificing accuracy. Provided that AI is applied within a carefully monitored, ethically governed framework, its integration into claims processing represents a genuine win-win opportunity.

But increasing the efficiency of claims processing is only half the story. Unless these savings translate into improved customer experience and satisfaction, the long-term gains may be illusory. Customers today demand – and expect – faster claims settlements. Organisations that can consistently meet these expectations are likely to enjoy a competitive edge, gaining not only customer satisfaction but also long-term loyalty. Conversely, companies that fail to deliver speedy settlements will, over time, see an increased rate of customer attrition, particularly among their high-value clients. Insurers that can harness new AI capabilities to manage these twin objectives – faster settlement with greater consistency – stand to capture both the operating and customer experience benefits.

References

1. Blier-Wong, C., Cossette, H., Lamontagne, L., & Marceau, E. (2021). Machine learning in P&C insurance: A review for pricing and reserving. Risks, 9(1), 4.

2. Eckert, C., & Osterrieder, K. (2020). How digitalization affects insurance companies: Overview and use cases of digital technologies. Zeitschrift für die gesamte Versicherungswissenschaft, 109, 333–360.

3. Mathew, M., Kunjumon, N. M., Lalji, R. M., Skariah, K. S., & Jeyakrishnan, V. (2020). Motor insurance claim processing and detection of fraudulent claims using machine learning. International Journal of Future Generation Communication and Networking, 13(3).

4. Yandamuri, U. S. (2024). AI-Driven Decision Support Systems for Operational Optimization in Hospitality Technology. Metallurgical and Materials Engineering.

5. Su, X., & Bai, M. (2020). Stochastic gradient boosting frequency-severity model of insurance claims. PLOS ONE, 15(8), e0238000.

6. Henckaerts, R., Côté, M.-P., Antonio, K., & Verbelen, R. (2021). Boosting insights in insurance tariff plans with tree-based machine learning methods. North American Actuarial Journal, 25(2), 255–285.

7. Gomes, C., Jin, Z., & Yang, H. (2021). Insurance fraud detection with unsupervised deep learning. Journal of Risk and Insurance, 88(3), 591–624.

8. Gabrielli, A. (2021). An individual claims reserving model for reported claims. European Actuarial Journal, 11(2), 541–577.

9. Cohen Sabban, I., Lopez, O., & Mercuzot, Y. (2021). Automatic analysis of insurance reports through deep neural networks to identify severe claims. Annals of Actuarial Science, 16(1), 42–67.

10. Denuit, M., Charpentier, A., & Trufin, J. (2021). Autocalibration and Tweedie-dominance for insurance pricing with machine learning. Insurance: Mathematics and Economics, 101, 485–497.

11. Mangalampalli, B. M., Bandi, V. D. V. K., Kolla, S. K., & Kumar, M. V. K. (2025). Towards Self-Evolving Healthcare Intelligence: Integrating Advanced Learning Systems with Real-Time Clinical Data Pipelines. Cultura: International Journal of Philosophy of Culture and Axiology, 22(12s), 464-486.

12. Severino, M. K., & Peng, Y. (2021). Machine learning algorithms for fraud prediction in property insurance: Empirical evidence using real-world microdata. Machine Learning with Applications, 5, 100074.

13. Eling, M., Nuessle, D., & Staubli, J. (2022). The impact of artificial intelligence along the insurance value chain and on the insurability of risks. The Geneva Papers on Risk and Insurance—Issues and Practice, 47, 205–241.

14. Xia, H., Zhou, Y., & Zhang, Z. (2022). Auto insurance fraud identification based on a CNN-LSTM fusion deep learning model. International Journal of Ad Hoc and Ubiquitous Computing, 39, 37–45.

15. Kolla, S. H., & Mangala, N. (2025). DESIGNING AUTONOMOUS LLM AGENT FRAMEWORKS USING GEN AI PIPELINES TO ENHANCE CUSTOMER SERVICE MANAGEMENT AND KNOWLEDGE WORKFLOWS. Lex Localis-Journal of Local Self-Government, 23 (S6), 9719–9733.

16. 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.

17. Dimri, A., Paul, A., Girish, D., Lee, P., Afra, S., & Jakubowski, A. (2022). A multi-input multi-label claims channeling system using insurance-based language models. Expert Systems with Applications, 202, 117166.

18. Crevecoeur, J., Robben, J., & Antonio, K. (2022). A hierarchical reserving model for reported non-life insurance claims. Insurance: Mathematics and Economics, 104, 158–184.

19. Kolla, S. K. (2025). Next-Generation Precision Healthcare: AI-Driven Clinical Intelligence, Predictive Analytics, and Adaptive Decision Support Systems. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 8(4), 12539-12552.

20. Debener, J., Heinke, V., & Kriebel, J. (2023). Detecting insurance fraud using supervised and unsupervised machine learning. Journal of Risk and Insurance, 90, 743–768.

21. Zhang, W., Shi, J., Wang, X., & Wynn, H. (2023). AI-powered decision-making in facilitating insurance claim dispute resolution. Annals of Operations Research.

22. Maiano, L., Montuschi, A., Caserio, M., Ferri, E., Kieffer, F., Germanò, C., Baiocco, L., Ricciardi Celsi, L., Amerini, I., & Anagnostopoulos, A. (2023). A deep-learning-based antifraud system for car-insurance claims. Expert Systems with Applications, 231, 120644.

23. Poufinas, T., Gogas, P., Papadimitriou, T., & Zaganidis, E. (2023). Machine learning in forecasting motor insurance claims. Risks, 11(9), 164.

24. Settipalli, L., & Gangadharan, G. R. (2023). WMTDBC: An unsupervised multivariate analysis model for fraud detection in health insurance claims. Expert Systems with Applications, 215, 119259.

25. Nabrawi, E., & Alanazi, A. (2023). Fraud detection in healthcare insurance claims using machine learning. Risks, 11(9), 160.

26. Davuluri, P. S. L. N. (2023). Integrating artificial intelligence into event-driven financial crime compliance platforms. International Journal of Finance, 36(6), 707-736.

27. Alam, A., & Prybutok, V. R. (2024). Use of responsible artificial intelligence to predict health insurance claims in the USA using machine learning algorithms. Exploration of Digital Health Technologies, 2, 30–45.

28. Abdulkadir, U. I., & Fernando, A. (2024). A deep learning model for insurance claims predictions. Journal on Artificial Intelligence, 6(1), 71–83.

29. Vorobyev, I. (2024). Fraud risk assessment in car insurance using claims graph features in machine learning. Expert Systems with Applications, 251, 124109.

30. Hamid, Z., Khalique, F., Mahmood, S., Daud, A., Bukhari, A., & Alshemaimri, B. (2024). Healthcare insurance fraud detection using data mining. BMC Medical Informatics and Decision Making, 24, 112.

31. 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.

32. Jaiswal, R., Gupta, S., & Tiwari, A. K. (2024). Big data and machine learning-based decision support system to reshape the vaticination of insurance claims. Technological Forecasting and Social Change, 209, 123829.

33. Bandi, V. D. V. K. AI-Based Anomaly Detection Frameworks in Distributed Enterprise Data Systems.

34. du Preez, A., Bhattacharya, S., Beling, P., & Bowen, E. (2025). Fraud detection in healthcare claims using machine learning: A systematic review. Artificial Intelligence in Medicine, 160, 103061.

35. Anand Kumar, P., & Sountharrajan, S. (2025). Insurance claims estimation and fraud detection with optimized deep learning techniques. Scientific Reports, 15, 27296.

36. Bhattacharya, S. B., Castignani, G. C., Masello, L., & Sheehan, B. (2025). AI revolution in insurance: Bridging research and reality. Frontiers in Artificial Intelligence, 8, 1568266.

Additional Files

Published

2026-03-13

How to Cite

AI’s Role in Modern Insurance Claims. (2026). Journal of Artificial Intelligence and Big Data Disciplines, 4(01). https://jaibdd.org/index.php/jaibddjournals/article/view/47