Intelligent Insurance Claims Management through AI and Automation

Tiejiang Sun; Xuguang Zhang; Mengdie Wang

doi:10.54097/egkzgw98

Authors

Tiejiang Sun
Xuguang Zhang
Mengdie Wang

DOI:

https://doi.org/10.54097/egkzgw98

Keywords:

Artificial Intelligence, Insurance, Robotic Process Automation, Natural Language Processing, Fraud Detection

Abstract

The insurance industry faces mounting pressure to modernize claims management processes amid rising claim volumes, increasing operational costs, and evolving customer expectations. Traditional manual claims processing systems suffer from inefficiencies, high error rates, and extended processing times that negatively impact both operational performance and customer satisfaction. Artificial intelligence (AI) and automation technologies have emerged as transformative solutions, offering unprecedented capabilities to streamline claims workflows, enhance decision accuracy, and improve customer experiences. This comprehensive review examines the application of AI and automation in insurance claims management, analyzing research published between 2019 and 2025. We explore diverse AI technologies including machine learning (ML), deep learning (DL), natural language processing (NLP), computer vision, and robotic process automation (RPA) across various insurance domains. The review reveals that AI-powered systems achieve substantial improvements in processing efficiency, with automation reducing processing times by up to 80% while maintaining or improving accuracy. Deep learning architectures demonstrate particular effectiveness in complex tasks such as fraud detection, damage assessment, and claim severity prediction. However, significant challenges persist, including data quality concerns, integration complexities with legacy systems, model interpretability requirements, and ethical considerations regarding algorithmic decision-making. We identify emerging trends including explainable AI frameworks, federated learning for privacy preservation, and hybrid human-AI collaboration models. This review contributes to understanding the current state and future trajectory of intelligent claims management systems while highlighting critical implementation considerations for insurance organizations.

References

[1] du Preez A, Bhattacharya S, Beling P, Bowen E. Fraud detection in healthcare claims using machine learning: A systematic review. Artificial Intelligence in Medicine. 2025; 160: 103061.

[2] Woodson VL. Evaluating Fraudulent Auto Insurance Claims: The Role of Technology and Law Enforcement in Detection and Prevention. Saint Leo University; 2024.

[3] Williamson B. Policy networks, performance metrics and platform markets: Charting the expanding data infrastructure of higher education. British Journal of Educational Technology. 2019;50(6):2794-2809.

[4] Becker J, Efstathiades A, Portmann J, Zeier Röschmann A. Data competences in the insurance industry. 2024.

[5] Bello OA, Olufemi K. Artificial intelligence in fraud prevention: Exploring techniques and applications challenges and opportunities. Computer Science and IT Research Journal. 2024;5(6):1505-1520.

[6] Taye MM. Understanding of machine learning with deep learning: architectures, workflow, applications and future directions. Computers. 2023;12(5):91.

[7] Vallarino D. Detecting Financial Fraud with Hybrid Deep Learning: A Mix-of-Experts Approach to Sequential and Anomalous Patterns. arXiv preprint arXiv:2504.03750. 2025.

[8] Toufik G, Khaldi Y, Pandey PS, Abusal YA. Advanced fraud detection in Card-Based financial systems using a bidirectional Lstm-Gru ensemble model. Applied Computer Science. 2024;20(3).

[9] Azad T, William P. Fraud detection in healthcare billing and claims. 2024.

[10] Vemulapalli G. Fighting fraud with algorithms: AI solutions for claim detection and revolutionizing fraud detection in insurance. In: Artificial Intelligence and Machine Learning for Sustainable Development. CRC Press; 2024. p. 125-140.

[11] Hamid Z, Khalique F, Mahmood S, Daud A, Bukhari A, Alshemaimri B. Healthcare insurance fraud detection using data mining. BMC Medical Informatics and Decision Making. 2024;24:112.

[12] Vyas S, Serasiya S. Fraud detection in insurance claim system: A review. In: 2022 Second International Conference on Artificial Intelligence and Smart Energy. IEEE; 2022. p. 922-927.

[13] Mwangi E. Employing ai/ml to determine and mitigate fraud in the insurance industry. Available at SSRN 4907329. 2024.

[14] Beaulac C, Rosenthal JS. Predicting university students' academic success and major using random forests. Research in Higher Education. 2019;60(7):1048-1064.

[15] Linardatos P, Papastefanopoulos V, Kotsiantis S. Explainable ai: A review of machine learning interpretability methods. Entropy. 2020;23(1):18.

[16] Barredo Arrieta A, Díaz-Rodríguez N, Del Ser J, et al. Explainable Artificial Intelligence: Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion. 2020;58:82-115.

[17] Fernandes E, Holanda M, Victorino M, Borges V, Carvalho R, Van Erven G. Educational data mining: Predictive analysis of academic performance of public school students in the capital of Brazil. Journal of Business Research. 2019;94:335-343.

[18] Sathe MT, Adamuthe AC. Comparative study of supervised algorithms for prediction of students' performance. International Journal of Modern Education and Computer Science. 2021;13(1):1-21.

[19] Chen Y, Zhao C, Xu Y, Nie C, Zhang Y. Year-over-Year Developments in Financial Fraud Detection via Deep Learning: A Systematic Literature Review. arXiv preprint arXiv:2502.00201. 2025.

[20] Mahveen Z. Optimizing fraud detection in healthcare: A hybrid machine learning approach. 2025.

[21] Schrijver CJ, Tan S, Boerkamp BJ, Simons M. Automobile insurance fraud detection using data mining: A systematic literature review. Expert Systems with Applications. 2024;213:119153.

[22] Subudhi S, Panigrahi S. Use of optimized Fuzzy C-Means clustering and supervised classifiers for automobile insurance fraud detection. Journal of King Saud University - Computer and Information Sciences. 2020;32(5):568-575.

[23] Pawar K, Attar V. Deep learning approaches for video-based anomalous activity detection. World Wide Web. 2019;22(2):571-601.

[24] Ghosh K, Bellinger C, Corizzo R, Branco P, Krawczyk B, Japkowicz N. The class imbalance problem in deep learning. Machine Learning. 2024;113(7):4845-4901.

[25] Khalil AA, Liu Z, Fathalla A, Ali A, Salah A. Machine Learning based Method for Insurance Fraud Detection on Class Imbalance Datasets with Missing Values. IEEE Access. 2024.

[26] Badr BE, Altawil I, Almomani M, Al-Saadi M, Alkhurainej M. Fault Diagnosis of Three-Phase Induction Motors Using Convolutional Neural Networks. Mathematical Modelling of Engineering Problems. 2023;10(5).

[27] Fursov I, Kovtun E, Rivera-Castro R, Zaytsev A, Khasyanov R, Spindler M, Burnaev E. Sequence embeddings help detect insurance fraud. IEEE Access. 2022;10:54326-54339.

[28] Rahman MM. DATA-DRIVEN GRAPH NEURAL NETWORK MODELS FOR DETECTING FRAUDULENT INSURANCE CLAIMS IN HEALTHCARE SYSTEMS. American Journal of Interdisciplinary Studies. 2025;6(1):263-294.

[29] Tsiakmaki M, Kostopoulos G, Kotsiantis S, Ragos O. Implementing AutoML in educational data mining for prediction tasks. Applied Sciences. 2019;10(1):90.

[30] Xia P, Zhou H, Zhang L. Auto insurance fraud identification based on a CNN-LSTM fusion deep learning model. International Journal of Ad Hoc and Ubiquitous Computing. 2022;39(1-2):37-49.

[31] Reddy NM, Sharada KA, Pilli D, Paranthaman RN, Reddy KS, Chauhan A. CNN-bidirectional LSTM based approach for financial fraud detection and prevention system. In: 2023 International Conference on Sustainable Computing and Smart Systems. IEEE; 2023. p. 541-546.

[32] Romero C, Ventura S. Educational data mining and learning analytics: An updated survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery. 2020;10(3):e1355.

[33] Alyahyan E, Düştegör D. Predicting academic success in higher education: literature review and best practices. International Journal of Educational Technology in Higher Education. 2020;17(1):3.

[34] Suresh S, Mohan S. ROI-based feature learning for efficient true positive prediction using convolutional neural network for lung cancer diagnosis. Neural Computing and Applications. 2020;32(20):15989-16009.

[35] Sahu A, Kumar R, Behera RK, Rath SK. Blockchain and machine learning based secure driver behavior-centric insurance model for electric vehicles. IEEE Transactions on Intelligent Transportation Systems. 2024;25(8):9632-9645.

[36] Azeem A, Ismail I, Mohani SS, Danyaro KU, Hussain U, Shabbir S, Jusoh RZ. Mitigating concept drift challenges in evolving smart grids: An adaptive ensemble LSTM for enhanced load forecasting. Energy Reports. 2025;13:1369-1383.

[37] Dunka V. AI-Driven Claims Fraud Detection Using Hybrid Deep Learning Models: Integrating Convolutional Neural Networks and Recurrent Neural Networks for Real-Time Fraud Detection in Insurance Claims. Essex Journal of AI Ethics and Responsible Innovation. 2023;3:276-311.

[38] Al Doulat A, Ayo-Bali OE, Shaik S. Fraud Detection in Insurance Claims Using Supervised Machine Learning Models. In: 2025 International Conference on Smart Applications, Communications and Networking. IEEE; 2025. p. 1-7.

[39] Wang Z, Chen X, Wu Y, Jiang L, Lin S, Qiu G. A robust and interpretable ensemble machine learning model for predicting healthcare insurance fraud. Scientific Reports. 2025;15(1):218.

[40] Chidananda A. Credit Card Fraud Detection Using Hybrid Deep Learning CNN-LSTM and CNN-GRU Models. California State University Northridge; 2025.

[41] Abakarim Y, Lahby M, Attioui A. A bagged ensemble convolutional neural networks approach to recognize insurance claim frauds. Applied System Innovation. 2023;6(1):20.

[42] Wang B, Kong W, Guan H, Xiong NN. Air quality forecasting based on gated recurrent long short term memory model in Internet of Things. IEEE Access. 2019;7:69524-69534.

[43] Nosouhian S, Nosouhian F, Khoshouei AK. A review of recurrent neural network architecture for sequence learning: Comparison between LSTM and GRU. 2021.

[44] Wu Z, Pan S, Chen F, Long G, Zhang C, Yu PS. A comprehensive survey on graph neural networks. IEEE Transactions on Neural Networks and Learning Systems. 2021;32(1):4-24.

[45] Hong X, Wang H, Zhang Y, Liu J. Health insurance fraud detection based on multi-channel heterogeneous graph structure learning. Heliyon. 2024;10(9):e30045.

[46] Dou Y, Liu Z, Sun L, Deng Y, Peng H, Yu PS. Enhancing graph neural network-based fraud detectors against camouflaged fraudsters. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management. 2020. p. 315-324.

[47] Li A, Xiao F, Zhang C, Fan C. Attention-based interpretable neural network for building cooling load prediction. Applied Energy. 2021;299:117238.

[48] Farbmacher H, Ihle P, Schubert I, Winter J. Explainable attention network for fraud detection in claims management. Health Economics. 2022;31(12):2599-2615.

[49] Hospedales T, Antoniou A, Micaelli P, Storkey A. Meta-learning in neural networks: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2022;44(9):5149-5169.

[50] Ali-Gombe A, Elyan E. MFC-GAN: Class-imbalanced dataset classification using multiple fake class generative adversarial network. Neurocomputing. 2019;361:212-221.

[51] Berahmand K, Daneshfar F, Salehi ES, Li Y, Xu Y. Autoencoders and their applications in machine learning: a survey. Artificial Intelligence Review. 2024;57(2):28.

[52] Powers DM. Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation. arXiv preprint arXiv:2010.16061. 2020.

[53] Beneish MD, Vorst P. The cost of fraud prediction errors. The Accounting Review. 2022;97(6):91-121.

[54] Sofaer HR, Hoeting JA, Jarnevich CS. The area under the precision‐recall curve as a performance metric for rare binary events. Methods in Ecology and Evolution. 2019;10(4):565-577.

[55] Liu Y, Ao X, Qin Z, Chi J, Feng J, Yang H, He Q. Pick and choose: A GNN-based imbalanced learning approach for fraud detection. In: Proceedings of the Web Conference 2021. 2021. p. 3168-3177.

[56] Steffens, T. (2020). Attribution of Advanced Persistent Threats: How to Identify the Actors Behind Cyber-Espionage. Springer Nature.

[57] Ijiga OM, Idoko IP, Ebiega GI, Olajide FI, Olatunde TI, Ukaegbu C. Harnessing adversarial machine learning for advanced threat detection: AI-driven strategies in cybersecurity risk assessment and fraud prevention. J Sci Technol. 2024;11:001-024.

[58] Chen S, Guo W. Auto-encoders in deep learning—a review with new perspectives. Mathematics. 2023;11(8):1777.

[59] Gonzales A, Guruswamy G, Smith SR. Synthetic data in health care: A narrative review. PLOS Digital Health. 2023;2(1):e0000082.

[60] Iman M, Arabnia HR, Rasheed K. A review of deep transfer learning and recent advancements. Technologies. 2023;11(2):40.

[61] Chauhan V, Yadav J. Bibliometric review of telematics-based automobile insurance: Mapping the landscape of research and knowledge. Accident Analysis & Prevention. 2024;196:107428.

[62] Monchka BA, Leung CK, Nickel NC, Lix LM. The effect of disease co-occurrence measurement on multimorbidity networks: a population-based study. BMC Medical Research Methodology. 2022;22(1):165.

[63] Hotz A, Sprecher E, Bastianelli L, Rodean J, Stringfellow I, Barkoudah E, et al. Categorization of a universal coding system to distinguish use of durable medical equipment and supplies in pediatric patients. JAMA Network Open. 2023;6(10):e2339449.

[64] Chan GK, Cummins MR, Taylor CS, Rambur B, Auerbach DI, Meadows-Oliver M, et al. An overview and policy implications of national nurse identifier systems: A call for unity and integration. Nursing Outlook. 2023;71(2):101892.

[65] Nesvijevskaia A, Ouillade S, Guilmin P, Zucker JD. The accuracy versus interpretability trade-off in fraud detection model. Data & Policy. 2021;3:e12.

[66] Foody GM. Challenges in the real world use of classification accuracy metrics: From recall and precision to the Matthews correlation coefficient. PloS One. 2023;18(10):e0291908.

[67] Richardson E, Trevizani R, Greenbaum J, Carter H, Nielsen M, Peters B. The ROC-AUC accurately assesses imbalanced datasets. Available at SSRN 4655233. 2023.

[68] Gunning, D., & Aha, D. (2019). DARPA’s explainable artificial intelligence (XAI) program. AI magazine, 40(2), 44-58.

[69] Lundberg SM, Erion G, Chen H, et al. From local explanations to global understanding with explainable AI for trees. Nature Machine Intelligence. 2020;2(1):56-67.

[70] Zafar MR, Khan N. Deterministic local interpretable model-agnostic explanations for stable explainability. Machine Learning and Knowledge Extraction. 2021;3(3):525-541.

[71] Dong P, Quan Z, Edwards B, Wang SH, Feng R, Wang T, et al. Privacy-Enhancing Collaborative Information Sharing through Federated Learning—A Case of the Insurance Industry. arXiv preprint arXiv:2402.14983. 2024.

[72] Mienye ID, Swart TG, Obaido G. Recurrent neural networks: A comprehensive review of architectures, variants, and applications. Information. 2024;15(9):517.

[73] Heald JB, Wolpert DM, Lengyel M. The computational and neural bases of context-dependent learning. Annual Review of Neuroscience. 2023;46(1):233-258.