Open Access
A Comprehensive Survey of Recent Advances Artificial Intelligence for Insurance Fraud Detection
4
Professor, Department of Computer Science and Engineering, OP Jindal University, Raigarh(C.G), India
Abstract
Insurance fraud is a big problem that the insurance industry is trying to solve. Economic loss, operational costs, and a decline in consumer trust are all consequences of insurance fraud. Traditional techniques of fraud detection, such as rule-based systems and human processes, frequently fail to detect more sophisticated fraud schemes. By analyzing complicated data in real-time, artificial intelligence (AI) has been shown to be an effective tool for automating the identification of fraud. Machine learning, explainable AI, federated learning, deep learning, reinforcement learning, natural language processing, and the most current approaches to AI methods in insurance fraud detection are included in this review. The paper also contains a discussion on traditional fraud detection methods, most popular insurance frauds, data preprocessing methods and some common metrics. Additionally, it looks at the latest research studies and points out the advantages and disadvantages of the available AI-driven approaches. Data quality, class imbalance, privacy preservation, model interpretability, scalability and regulatory compliance are among the key challenges that are critically analyzed.
Keywords
Insurance Fraud Detection,
Artificial Intelligence (AI),
Machine Learning
References
G. Schrijver, D. K. Sarmah, and M. El-hajj, “Automobile insurance fraud detection using data mining: A systematic literature review,” 2024. doi: 10.1016/j.iswa.2024.200340.
S. Kakkar, B. Makkena, A. Chaudhary, and A. Kakkar, “Explainable Machine Learning Models for Credit Scoring for Risk Forecasting in Big Financial Datasets,” in 2026 2nd International Conference on Cognitive Computing in Engineering, Communications, Sciences and Biomedical Health Informatics (IC3ECSBHI), Greater Noida, (Delhi NCR), India: IEEE, 2026, pp. 1384–1391, February. doi: 10.1109/IC3ECSBHI67834.2026.11469004.
S. R. Somula, “Intelligent Fraud Prevention: Implementing AML Solutions For Mcca Reimbursement Claims Protection,” Int. J. Comput. Eng. Technol., vol. 16, no. 1, pp. 3609–3622, Feb. 2025, doi: 10.34218/IJCET_16_01_249.
C. Phua, D. Alahakoon, and V. Lee, “Minority report in fraud detection,” ACM SIGKDD Explor. Newsl., vol. 6, no. 1, pp. 50–59, Jun. 2004, doi: 10.1145/1007730.1007738.
H. K. Yadav, “Design and Implement Machine Learning Based Predictive Analytics for Road Safety and Accident Prevention,” in 2026 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE, Mar. 2026, pp. 1–6. doi: 10.1109/IATMSI68868.2026.11465965.
A. K. I. Hassan and A. Abraham, “Modeling insurance fraud detection using imbalanced data classification,” in Advances in Intelligent Systems and Computing, 2016. doi: 10.1007/978-3-319-27400-3_11.
P. Ashok and A. S. Durge, “Fraud Detection and Prevention in Healthcare Insurance Claims Using Machine Learning Regression Models,” in 2025 International Conference on Data Science and Business Systems, ICDSBS 2025, 2025. doi: 10.1109/ICDSBS63635.2025.11031751.
P. C. Shekhar, “Testing For The Unexpected: Ensuring Insurance System Stability During Covid-19,” J. Artif. Intell. Mach. Learn. Data Sci., vol. 1, no. 1, pp. 1–15, November, 2022, doi: doi.org/10.51219/JAIMLD/Chandra-shekhar-pareek/375.
S. Thiprungsri and M. Vasarhelyi, “Cluster Analysis for Anomaly Detection in Accounting Data: An Audit Approach,” Int. J. Digit. Account. Res., vol. 11, 2011, doi: 10.4192/1577-8517-v11_4.
B. Mohanty, “Role of artificial intelligence in financial fraud detection,” 2023.
A. Warrier and K. Abhilash, “Autonomous Agentic AI for Clinical Workflow Orchestration: Self-Managing Healthcare Operations,” in 2025 6th International Conference on IoT Based Control Networks and Intelligent Systems (ICICNIS), Bengaluru, India: IEEE, 2025, pp. 1450–1457, December. doi: 10.1109/ICICNIS66685.2025.11315712.
J. Xu, T. Yang, S. Zhuang, H. Li, and W. Lu, “AI-based financial transaction monitoring and fraud prevention with behaviour prediction,” Appl. Comput. Eng., 2024, doi: 10.54254/2755-2721/77/2024ma0068.
R. Palwe, “Adaptive human: AI decision support for high-stakes financial advice,” Int. J. Comput. Artif. Intell., vol. 6, no. 2, pp. 385–392, Jul. 2025, doi: 10.33545/27076571.2025.v6.i2e.226.
P. Adhikari, P. Hamal, and Francis Baidoo Jnr, “Impact and regulations of AI on labor markets and employment in USA,” Int. J. Sci. Res. Arch., 2024, doi: 10.30574/ijsra.2024.13.1.1670.
B. Sharan, M. Hassan, V. D. Vani, V. H. Raj, Ginni Nijhawan, and P. P. Pawar, “Machine Learning-Based Fraud Detection System for Insurance Claims in IoT Environment,” in 2024 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI), 2024, pp. 1–5. doi: 10.1109/ACCAI61061.2024.10601786.
P. Adhikari, P. Hamal, and F. B. Jnr, “Artificial Intelligence in fraud detection: Revolutionizing financial security,” Int. J. Sci. Res. Arch., vol. 13, no. 01, pp. 1457–1472, 2024.
P. Chandra Shekhar, “Next-Gen Test Automation in Life Insurance: Self-Healing Frameworks,” Int. J. Sci. Res. Eng. Manag., vol. 5, no. 7, pp. 1–13, 2021, doi: 10.55041/IJSREM9554.
S. Farzai and A. Ghorbani, “Fraud Detection in Automobile Insurance using a Data Mining Based Approach,” Int. J. Mechatronics, Electr. Comput. Technol., 2018.
D. Vineela*, P. Swathi, T. Sritha, and K. Ashesh, “Fraud Detection in Health Insurance Claims using Machine Learning Algorithms,” Int. J. Recent Technol. Eng., 2020, doi: 10.35940/ijrte.e6485.018520.
S. Subudhi and S. Panigrahi, “Detection of Automobile Insurance Fraud Using Feature Selection and Data Mining Techniques,” Int. J. Rough Sets Data Anal., 2018, doi: 10.4018/ijrsda.2018070101.
N. Duwadi and A. Sharma, “Comparative Study of Machine Learning Techniques for Insurance Fraud Detection,” Aviat. Electron. Inf. Technol. Telecommun. Electr. Control., 2024, doi: 10.28989/avitec.v6i2.2340.
H. L. Sithic and T. Balasubramanian, “Survey of Insurance Fraud Detection Using Data Mining Techniques,” no. 3, pp. 62–65, 2013.
B. Clark, L. Mitchell, and G. Christopher, “Insurance Fraud Detection: Insights from Artificial Intelligence and Machine Learning,” 2025.
O. A. Bello, A. Folorunso, and J. Onwuchekwa, “A comprehensive framework for strengthening USA financial cybersecurity: Integrating machine learning and AI in fraud detection systems,” Eur. J. Comput. Sci. Inf. Technol., 2023.
M. G. Kibria and M. Sevkli, “Application of Deep Learning for Credit Card Approval: A Comparison with Two Machine Learning Techniques,” Int. J. Mach. Learn. Comput., 2021, doi: 10.18178/ijmlc.2021.11.4.1049.
M. Vriscu, “Harnessing Artificial Intelligence for Risk Assessment and Fraud Detection in Insurance: A Modern Approach to Predictive Modeling,” Proc. Int. Conf. Bus. Excell., 2025, doi: 10.2478/picbe-2025-0179.
S. Ragaupathy and M. Mittal, “Next-Generation Crm In Healthcare: The Role of AI, Predictive Analytics, And Patient Journey Mapping,” Int. J. Cust. Relatsh. Manag., vol. 4, no. 2, pp. 1–24, July-December, 2025, doi: 10.34218/IJCRM_04_02_001.
D. Coals, A. John, and K. Khan, “Detecting Insurance Fraud using AI: A Case Study of Claims Processing,” 2025.
H. Narne, “Machine Learning for Health Insurance Fraud Detection: Techniques, Insights, and Implementation Strategies,” Int. J. Res. Anal. Rev., 2024.
M. Abdul Salam, K. M. Fouad, D. L. Elbably, and S. M. Elsayed, “Federated learning model for credit card fraud detection with data balancing techniques,” Neural Comput. Appl., 2024, doi: 10.1007/s00521-023-09410-2.
S. O. Boufaida, A. Benmachiche, M. Maatallah, and C. Chemam, “An Extensive Examination of Varied Approaches in E-Learning and MOOC Research: A Thorough Overview,” in PAIS 2024 - Proceedings: 6th International Conference on Pattern Analysis and Intelligent Systems, 2024. doi: 10.1109/PAIS62114.2024.10541129.
I. Soualmia, S. Maalem, A. Benmachiche, K. Rais, and M. Derdour, “Comparative Survey of AI-Driven Credit Card Fraud Detection: Machine Learning, Deep Learning and Hybrid Systems,” in 2025 International Conference on Networking and Advanced Systems (ICNAS), IEEE, Oct. 2025, pp. 1–9. doi: 10.1109/ICNAS68168.2025.11298125.
S. K. Madishetty, “Cyber Resilience and DevOps in Operating Rooms: Securing Connected Embedded Medical Devices in High Risk Clinical Environments,” J. Electr. Syst., vol. 19, no. 3, pp. 244–253, Jan. 2024, doi: 10.52783/jes.9597.
R. Gupta, “Artificial Intelligence (AI) in Insurance: A Futuristic Approach,” Rev. Ref. Res. Journal), 2020.
R. J. Bolton and D. J. Hand, “Statistical fraud detection: A review,” Stat. Sci., vol. 17, no. 3, pp. 235–255, 2002, doi: 10.1214/ss/1042727940.
K. Crawford and T. Paglen, “Excavating AI: the politics of images in machine learning training sets,” AI Soc., 2021, doi: 10.1007/s00146-021-01162-8.
I. I. J. Rifka Alkhilyatul Ma’rifat, I Made Suraharta, “Artificial Intelligence In Insurance Processes: Fraud Detection And Risk Mitigation,” vol. 2, pp. 306–312, 2024.
V. Priya, R. S. Priya, and D. S. Priya, “An Intelligent Deep Learning Framework for Scalable Insurance Fraud Detection and Risk Assessment,” in 2026 4th International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS), 2026, pp. 172–179. doi: 10.1109/ICSSAS68835.2026.11559352.
S. Peykani and E. Kugu, “Explainable Artificial Intelligence (XAI) In Insurance Fraud Detection,” in 2026 5th International Informatics and Software Engineering Conference (IISEC), 2026, pp. 537–542. doi: 10.1109/IISEC69317.2026.11418497.
R. Agarwal, D. Kalsi, P. Jain, P. Gupta, and R. Goel, “Car Insurance Fraud Detection using Machine Learning Models,” in 2025 International Conference on Next Generation Information System Engineering (NGISE), 2025, pp. 1–8. doi: 10.1109/NGISE64126.2025.11085234.
P. K. Gupta, R. Bhatnagar, S. Tyagi, A. Jain, A. Panwar, and A. K. Dubey, “Machine Learning Algorithms for Auto Insurance Fraud Detection: Comparative Study,” in 2024 IEEE 1st International Conference on Green Industrial Electronics and Sustainable Technologies, GIEST 2024, 2024. doi: 10.1109/GIEST62955.2024.10959939.
K. P. Rijanto, D. A. Putri, P. Calvin, I. S. Edbert, and D. Suhartono, “Machine Learning Algorithms with Oversampling Methods for Automobile Insurance Fraud Detection,” in 2023 IEEE 21st Student Conference on Research and Development (SCOReD), 2023, pp. 138–143. doi: 10.1109/SCOReD60679.2023.10563582.
B. K. Sethi, D. Singh, and P. K. Sarangi, “Medical Insurance Fraud Detection Based on Block Chain and Deep Learning Approach,” in 2022 International Conference on Disruptive Technologies for Multi-Disciplinary Research and Applications (CENTCON), 2022, pp. 103–106. doi: 10.1109/CENTCON56610.2022.10051253.
Most read articles by the same author(s)
- Mohammed Imran Choudhary, AI-Augmented Network-Forensics: Leveraging LLMs for Real-Time Threat Detection and Automated Response in Enterprise Environments , International Journal of Advanced Artificial Intelligence Research: Vol. 3 No. 09 (2026): Volume 03 Issue 09
- Sashank Siwakoti, Bhaskar Chaganti, Human-in-the-Loop Control Planes for Cortex Agents: Policy-Driven Escalation, Approval, and Evidence Capture , International Journal of Advanced Artificial Intelligence Research: Vol. 3 No. 09 (2026): Volume 03 Issue 09
- Priya Sharma, A Data-Centric Approach to Transforming Digital Retail Through Artificial Intelligence-Based Shopping Systems , International Journal of Advanced Artificial Intelligence Research: Vol. 3 No. 09 (2026): Volume 03 Issue 09
- Sonam Kumari, Enhancing Clinical Decision-Making Using Generative AI-Powered Knowledge Retrieval Systems: A Review of Emerging Approaches and Challenges , International Journal of Advanced Artificial Intelligence Research: Vol. 3 No. 08 (2026): Volume 03 Issue 08
- Amit Kumar Dhariwal, Comprehensive Study on the Use of Artificial Intelligence to Minimize Bias in Healthcare Succession Management , International Journal of Advanced Artificial Intelligence Research: Vol. 3 No. 08 (2026): Volume 03 Issue 08
- Suprajyotsna Dasari , Automated Testing Techniques for Enterprise Software Systems with GenAI Integration , International Journal of Advanced Artificial Intelligence Research: Vol. 3 No. 09 (2026): Volume 03 Issue 09
- Nguyen Minh Anh, Tran Quoc Bao, Unsupervised Learning Framework for Country Clustering Based on Agricultural Import Patterns , International Journal of Advanced Artificial Intelligence Research: Vol. 3 No. 09 (2026): Volume 03 Issue 09
- Nimal Perera, Anjali Fernando, Robust Browser Fingerprinting Under Adversarial Conditions: An AI-Driven Detection and Defense Architecture , International Journal of Advanced Artificial Intelligence Research: Vol. 3 No. 09 (2026): Volume 03 Issue 09
- Severov Arseni Vasilievich, Artyom V. Smirnov, Architecting Real-Time Risk Stratification in the Insurance Sector: A Deep Convolutional and Recurrent Neural Network Framework for Dynamic Predictive Modeling , International Journal of Advanced Artificial Intelligence Research: Vol. 2 No. 10 (2025): Volume 02 Issue 10
- Dr. Amit Jain, A Comprehensive Survey of Recent Advances Artificial Intelligence for Insurance Fraud Detection , International Journal of Advanced Artificial Intelligence Research: Vol. 3 No. 08 (2026): Volume 03 Issue 08
Similar Articles
- Dr. Aris Thorne, Generating Dual-Identity Face Impersonations with Generative Adversarial Networks: An Adversarial Attack Methodology , International Journal of Advanced Artificial Intelligence Research: Vol. 2 No. 10 (2025): Volume 02 Issue 10
- Anjali Kale, FX Hedging Algorithms for Crypto-Native Companies , International Journal of Advanced Artificial Intelligence Research: Vol. 2 No. 10 (2025): Volume 02 Issue 10
- Suprajyotsna Dasari , Automated Testing Techniques for Enterprise Software Systems with GenAI Integration , International Journal of Advanced Artificial Intelligence Research: Vol. 3 No. 09 (2026): Volume 03 Issue 09
- Jeyakumar Ramachandran, Confidential AI Cloud Architecture for Secure Enterprise Data Processing and Intelligent Workloads , International Journal of Advanced Artificial Intelligence Research: Vol. 2 No. 12 (2025): Volume 02 Issue 12
- Farhad Nouri, Dr. Mohammadreza Nouri, ADAPTIVE SIMILARITY-DRIVEN APPROACHES FOR CONTINUAL LEARNING: BRIDGING TASK-AWARE AND TASK-FREE PARADIGMS , International Journal of Advanced Artificial Intelligence Research: Vol. 2 No. 01 (2025): Volume 02 Issue 01
- Mohammed Imran Choudhary, AI-Augmented Network-Forensics: Leveraging LLMs for Real-Time Threat Detection and Automated Response in Enterprise Environments , International Journal of Advanced Artificial Intelligence Research: Vol. 3 No. 09 (2026): Volume 03 Issue 09
- Dr. Arvind Patel, Anamika Mishra, INTELLIGENT BARGAINING AGENTS IN DIGITAL MARKETPLACES: A FUSION OF REINFORCEMENT LEARNING AND GAME-THEORETIC PRINCIPLES , International Journal of Advanced Artificial Intelligence Research: Vol. 2 No. 03 (2025): Volume 02 Issue 03
- Leon Ficsher, Resilient Embedded Architectures for Safety-Critical Automotive Systems: Integrating Lockstep Fault Tolerance, Cybersecurity Assurance, And Software-Defined Platforms , International Journal of Advanced Artificial Intelligence Research: Vol. 1 No. 01 (2024): Volume 01 Issue 01
- Ronak Jani, Automated Monitoring and Self-Healing Mechanisms in High-Availability Cloud Databases , International Journal of Advanced Artificial Intelligence Research: Vol. 3 No. 04 (2026): Volume 03 Issue 04
- Adrian Velasco, Meera Narayan, REVOLUTIONIZING SILICON PHOTONIC DEVICE DESIGN THROUGH DEEP GENERATIVE MODELS: AN INVERSE APPROACH AND EMERGING TRENDS , International Journal of Advanced Artificial Intelligence Research: Vol. 2 No. 06 (2025): Volume 02 Issue 06
You may also start an advanced similarity search for this article.