Blockchain for Fraud Detection in P&C Insurance Claims
Keywords:
Blockchain integration, insurance fraud preventionAbstract
Blockchain technology has proven to be a game-changer for industries that rely heavily on trust and transparency, making it a natural fit for the property and casualty (P&C) insurance sector, where fraud remains a persistent & costly issue. Fraudulent claims drain financial resources and create inefficiencies that drive up premiums for honest policyholders. By integrating blockchain into the insurance claims process, insurers can significantly enhance transparency and accountability. Blockchain’s decentralized and immutable nature ensures that every transaction and modification in the claims process is securely recorded & auditable, reducing opportunities for fraudulent activities. Additionally, smart contracts powered by blockchain can automate critical steps in claims processing, such as policy verification and payout calculations, drastically reducing processing times and minimizing human error. This efficiency fosters better collaboration among stakeholders, including insurers, reinsurers, and policyholders, by providing a single, trusted source of truth. Real-world applications of blockchain in insurance, such as shared fraud registries and tamper-proof customer records, demonstrate the technology’s potential to streamline operations and improve fraud detection. However, challenges such as the need for industry-wide standardization, regulatory compliance, & the technical expertise required for implementation may hinder widespread adoption. Despite these hurdles, blockchain holds immense promise as a solution for creating a more secure, efficient, and customer-centric claims process in the P&C insurance industry. By addressing fraud and inefficiencies head-on, blockchain not only saves costs for insurers but also builds lasting trust with customers, shaping the future of insurance claims management.
Downloads
References
Akande, A. (2018). Disruptive power of blockchain on the insurance industry. Master's Thesis, Universiti of Tartu, Institute of Computer Science, Tartu.
Shetty, A., Shetty, A. D., Pai, R. Y., Rao, R. R., Bhandary, R., Shetty, J., ... & Dsouza, K. J. (2022). Block chain application in insurance services: A systematic review of the evidence. SAGE Open, 12(1), 21582440221079877.
Veeramani, K., & Jaganathan, S. (2021). Use-case of blockchain in cybercrime and cyberattack. In Confluence of AI, machine, and deep learning in cyber forensics (pp. 145-163). IGI Global.
Henry, K. J., & Hogan, B. W. (2018). Insurance and Blockchain: What Policyholders Need to Know. Bradley., available online at: https://www. bradley. com/insights/publications/2018/02/insurance-andblockchain-whatpolicyholders-need-to-know, Accessed on [01.03. 2021.].
Chekriy, S., & Mukhin, Y. (2018). Blockchain platform for insurance-related products. Glass Cube, 1-33.
Trivedi, S., & Malik, R. (2022). Blockchain technology as an emerging technology in the insurance market. In Big Data: A Game Changer for Insurance Industry (pp. 81-100). Emerald Publishing Limited.
Sayegh, K., & Desoky, M. (2019). Blockchain application in insurance and reinsurance. France: Skema Business School.
Yıldırım, İ. (2020). Blockchain in the insurance industry: use cases and applications. In Cross-industry use of blockchain technology and opportunities for the future (pp. 117-130). IGI Global.
Malhotra, R. K., Gupta, C., & Jindal, P. (2022). Blockchain and Smart Contracts for Insurance Industry. Blockchain Technology in Corporate Governance: Transforming Business and Industries, 239-252.
Loukil, F., Boukadi, K., Hussain, R., & Abed, M. (2021). Ciosy: A collaborative blockchain-based insurance system. Electronics, 10(11), 1343.
Xiao, Z., Li, Z., Yang, Y., Chen, P., Liu, R. W., Jing, W., ... & Goh, R. S. M. (2020). Blockchain and IoT for insurance: a case study and cyberinfrastructure solution on fine-grained transportation insurance. IEEE Transactions on Computational Social Systems, 7(6), 1409-1422.
Zanke, P. (2021). Enhancing Claims Processing Efficiency Through Data Analytics in Property & Casualty Insurance. Journal of Science & Technology, 2(3), 69-92.
Abramowicz, M. (2019). Blockchain-based insurance. Blockchain and the Constitution of a New Financial Order: Legal and Political Challenges (Ioannis Lianos et al. eds., 2019, Forthcoming)., GWU Law School Public Law Research Paper, (2019-12).
Kar, A. K., & Navin, L. (2021). Diffusion of blockchain in insurance industry: An analysis through the review of academic and trade literature. Telematics and Informatics, 58, 101532.
Katari, A., & Vangala, R. Data Privacy and Compliance in Cloud Data Management for Fintech.
Katari, A., Ankam, M., & Shankar, R. Data Versioning and Time Travel In Delta Lake for Financial Services: Use Cases and Implementation.
Katari, A. (2022). Performance Optimization in Delta Lake for Financial Data: Techniques and Best Practices. MZ Computing Journal, 3(2).
Katari, A., Muthsyala, A., & Allam, H. HYBRID CLOUD ARCHITECTURES FOR FINANCIAL DATA LAKES: DESIGN PATTERNS AND USE CASES.
Babulal Shaik. Automating Compliance in Amazon EKS Clusters With Custom Policies . Journal of Artificial Intelligence Research and Applications, vol. 1, no. 1, Jan. 2021, pp. 587-10
Babulal Shaik. Developing Predictive Autoscaling Algorithms for Variable Traffic Patterns . Journal of Bioinformatics and Artificial Intelligence, vol. 1, no. 2, July 2021, pp. 71-90
Babulal Shaik, et al. Automating Zero-Downtime Deployments in Kubernetes on Amazon EKS . Journal of AI-Assisted Scientific Discovery, vol. 1, no. 2, Oct. 2021, pp. 355-77
Nookala, G., Gade, K. R., Dulam, N., & Thumburu, S. K. R. (2022). The Shift Towards Distributed Data Architectures in Cloud Environments. Innovative Computer Sciences Journal, 8(1).
Nookala, G. (2022). Improving Business Intelligence through Agile Data Modeling: A Case Study. Journal of Computational Innovation, 2(1).
Nookala, G., Gade, K. R., Dulam, N., & Thumburu, S. K. R. (2021). Unified Data Architectures: Blending Data Lake, Data Warehouse, and Data Mart Architectures. MZ Computing Journal, 2(2).
Nookala, G. (2021). Automated Data Warehouse Optimization Using Machine Learning Algorithms. Journal of Computational Innovation, 1(1).
Nookala, G., Gade, K. R., Dulam, N., & Thumburu, S. K. R. (2020). Automating ETL Processes in Modern Cloud Data Warehouses Using AI. MZ Computing Journal, 1(2).
Boda, V. V. R., & Immaneni, J. (2022). Optimizing CI/CD in Healthcare: Tried and True Techniques. Innovative Computer Sciences Journal, 8(1).
Immaneni, J. (2022). End-to-End MLOps in Financial Services: Resilient Machine Learning with Kubernetes. Journal of Computational Innovation, 2(1).
Boda, V. V. R., & Immaneni, J. (2021). Healthcare in the Fast Lane: How Kubernetes and Microservices Are Making It Happen. Innovative Computer Sciences Journal, 7(1).
Immaneni, J. (2021). Using Swarm Intelligence and Graph Databases for Real-Time Fraud Detection. Journal of Computational Innovation, 1(1).
Gade, K. R. (2022). Data Analytics: Data Fabric Architecture and Its Benefits for Data Management. MZ Computing Journal, 3(2).
Gade, K. R. (2022). Data Modeling for the Modern Enterprise: Navigating Complexity and Uncertainty. Innovative Engineering Sciences Journal, 2(1).
Gade, K. R. (2022). Migrations: AWS Cloud Optimization Strategies to Reduce Costs and Improve Performance. MZ Computing Journal, 3(1).
Gade, K. R. (2022). Cloud-Native Architecture: Security Challenges and Best Practices in Cloud-Native Environments. Journal of Computing and Information Technology, 2(1).
Muneer Ahmed Salamkar. Batch Vs. Stream Processing: In-Depth Comparison of Technologies, With Insights on Selecting the Right Approach for Specific Use Cases. Distributed Learning and Broad Applications in Scientific Research, vol. 6, Feb. 2020
Muneer Ahmed Salamkar, and Karthik Allam. Data Integration Techniques: Exploring Tools and Methodologies for Harmonizing Data across Diverse Systems and Sources. Distributed Learning and Broad Applications in Scientific Research, vol. 6, June 2020
Muneer Ahmed Salamkar, et al. The Big Data Ecosystem: An Overview of Critical Technologies Like Hadoop, Spark, and Their Roles in Data Processing Landscapes. Journal of AI-Assisted Scientific Discovery, vol. 1, no. 2, Sept. 2021, pp. 355-77
Muneer Ahmed Salamkar. Scalable Data Architectures: Key Principles for Building Systems That Efficiently Manage Growing Data Volumes and Complexity. Journal of AI-Assisted Scientific Discovery, vol. 1, no. 1, Jan. 2021, pp. 251-70
Naresh Dulam, et al. “Data Mesh Best Practices: Governance, Domains, and Data Products”. Australian Journal of Machine Learning Research & Applications, vol. 2, no. 1, May 2022, pp. 524-47
Naresh Dulam, et al. “Apache Iceberg 1.0: The Future of Table Formats in Data Lakes”. Journal of AI-Assisted Scientific Discovery, vol. 2, no. 1, Feb. 2022, pp. 519-42
Naresh Dulam, et al. “Kubernetes at the Edge: Enabling AI and Big Data Workloads in Remote Locations”. Journal of AI-Assisted Scientific Discovery, vol. 2, no. 2, Oct. 2022, pp. 251-77
Naresh Dulam, et al. “Data Mesh and Data Governance: Finding the Balance”. Journal of AI-Assisted Scientific Discovery, vol. 2, no. 2, Dec. 2022, pp. 226-50
Thumburu, S. K. R. (2022). EDI and Blockchain in Supply Chain: A Security Analysis. Journal of Innovative Technologies, 5(1).
Thumburu, S. K. R. (2022). A Framework for Seamless EDI Migrations to the Cloud: Best Practices and Challenges. Innovative Engineering Sciences Journal, 2(1).
Thumburu, S. K. R. (2022). The Impact of Cloud Migration on EDI Costs and Performance. Innovative Engineering Sciences Journal, 2(1).
Thumburu, S. K. R. (2022). AI-Powered EDI Migration Tools: A Review. Innovative Computer Sciences Journal, 8(1).
Thumburu, S. K. R. (2022). Real-Time Data Transformation in EDI Architectures. Innovative Engineering Sciences Journal, 2(1).
Sarbaree Mishra. “A Reinforcement Learning Approach for Training Complex Decision Making Models”. Journal of AI-Assisted Scientific Discovery, vol. 2, no. 2, July 2022, pp. 329-52
Sarbaree Mishra, et al. “Leveraging in-Memory Computing for Speeding up Apache Spark and Hadoop Distributed Data Processing”. Journal of AI-Assisted Scientific Discovery, vol. 2, no. 2, Sept. 2022, pp. 304-28
Sarbaree Mishra. “Comparing Apache Iceberg and Databricks in Building Data Lakes and Mesh Architectures”. Journal of AI-Assisted Scientific Discovery, vol. 2, no. 2, Nov. 2022, pp. 278-03
Sarbaree Mishra. “Reducing Points of Failure - a Hybrid and Multi-Cloud Deployment Strategy With Snowflake”. Journal of AI-Assisted Scientific Discovery, vol. 2, no. 1, Jan. 2022, pp. 568-95
Sarbaree Mishra, et al. “A Domain Driven Data Architecture for Data Governance Strategies in the Enterprise”. Journal of AI-Assisted Scientific Discovery, vol. 2, no. 1, Apr. 2022, pp. 543-67
Komandla, V. Enhancing Product Development through Continuous Feedback Integration “Vineela Komandla”.
Komandla, V. Enhancing Security and Growth: Evaluating Password Vault Solutions for Fintech Companies.
Komandla, V. Strategic Feature Prioritization: Maximizing Value through User-Centric Roadmaps.
Komandla, V. Enhancing Security and Fraud Prevention in Fintech: Comprehensive Strategies for Secure Online Account Opening.
Komandla, Vineela. "Effective Onboarding and Engagement of New Customers: Personalized Strategies for Success." Available at SSRN 4983100 (2019).
