Vol. 3 No. 2 (2023): Journal of AI-Assisted Scientific Discovery
Articles

Smart Contracts in P&C Insurance: Opportunities and Challenges

PDF
  • Ravi Teja Madhala
Ravi Teja Madhala
Senior Software Developer Analyst at Mercury Insurance Services, LLC, USA
Cover

Published 04-07-2023

Keywords

  • Smart contracts,
  • blockchain technology
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Abstract

Smart contracts, underpinned by blockchain technology, are revolutionizing the property and casualty (P&C) insurance industry by addressing inefficiencies in claims processing and policy management. These self-executing contracts automate processes traditionally reliant on manual intervention, enabling faster claims resolution, reduced operational costs, and improved accuracy in policy administration. By embedding terms and conditions directly into the blockchain, smart contracts minimize human error, enhance transparency, & significantly mitigate fraud through immutable and tamper-proof records. This technology promises to transform customer experiences by eliminating intermediaries, ensuring quicker settlements, and fostering trust through verifiable and secure transactions. However, the integration of smart contracts in P&C insurance comes with its set of challenges. Regulatory compliance remains a significant hurdle, as global jurisdictions have yet to establish uniform standards for blockchain-based solutions. A clear legal framework must be introduced to ensure enforceability & data privacy certainty. On the technical front, limitations such as scalability issues, interoperability between legacy systems and blockchain platforms, and the substantial computational resources required to support blockchain networks pose considerable barriers to adoption. Additionally, the need for specialized expertise to implement and maintain innovative contract systems creates a steep learning curve for insurers. The integration process must also account for potential vulnerabilities in smart contracts that could be exploited, leading to unintended consequences. Despite these challenges, the opportunities are immense. P&C insurers can leverage smart contracts to drive innovation, streamline processes, and align with a more digital-first approach that aligns with customer expectations for speed and reliability. This exploration highlights the critical balance insurers must strike between embracing the transformative potential of intelligent contracts & addressing the complexities of implementation. By proactively navigating the regulatory landscape, investing in scalable and interoperable solutions, & fostering collaboration between industry stakeholders, insurers can unlock the full potential of smart contracts.

PDF

Downloads

Download data is not yet available.

References

  1. 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.
  2. Scherrer, J., & Salahshor, A. (2020). Smart Contracts, Insurtechs and the Future of Insurance.
  3. Borselli, A. (2020). Smart contracts in insurance: a law and futurology perspective (pp. 101-125). Springer International Publishing.
  4. Hoffmann, C. H. (2021). A double design-science perspective of entrepreneurship–the example of smart contracts in the insurance market. Journal of Work-Applied Management, 13(1), 69-87.
  5. Unsworth, R. (2019). Smart contract this! An assessment of the contractual landscape and the Herculean challenges it currently presents for “Self-executing” contracts. Legal tech, smart contracts and blockchain, 17-61.
  6. Nicoletti, B. (2020). Insurance 4.0: Benefits and challenges of digital transformation. Springer Nature.
  7. Broström, E., & Bengtsson, V. (2018). Growing Customer Loyalty in the light of Digitalization-A study on the Swedish P&C Insurance Industry.
  8. 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.
  9. Cousaert, S., Vadgama, N., & Xu, J. (2022). Token-based insurance solutions on blockchain. In Blockchains and the token economy: Theory and practice (pp. 237-260). Cham: Springer International Publishing.
  10. Stempel, J. W. (2005). Stempel on insurance contracts. Wolters Kluwer.
  11. Bosisio, R., Burchardi, K., Calvert, T., & Hauser, M. (2018). The first all-blockchain insurer. Boston Consulting Group.
  12. 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).
  13. Lin, L., & Chen, C. (2020). The promise and perils of InsurTech. Singapore Journal of Legal Studies, (Mar 2020), 115-142.
  14. Sayegh, K., & Desoky, M. (2019). Blockchain application in insurance and reinsurance. France: Skema Business School.
  15. Cohn, A., West, T., & Parker, C. (2016). Smart after all: Blockchain, smart contracts, parametric insurance, and smart energy grids. Geo. L. Tech. Rev., 1, 273.
  16. Katari, A., & Vangala, R. Data Privacy and Compliance in Cloud Data Management for Fintech.
  17. Katari, A., Ankam, M., & Shankar, R. Data Versioning and Time Travel In Delta Lake for Financial Services: Use Cases and Implementation.
  18. Katari, A. (2022). Performance Optimization in Delta Lake for Financial Data: Techniques and Best Practices. MZ Computing Journal, 3(2).
  19. Katari, A., Muthsyala, A., & Allam, H. HYBRID CLOUD ARCHITECTURES FOR FINANCIAL DATA LAKES: DESIGN PATTERNS AND USE CASES.
  20. Katari, A. Conflict Resolution Strategies in Financial Data Replication Systems.
  21. 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
  22. 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
  23. 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
  24. 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).
  25. Nookala, G. (2022). Improving Business Intelligence through Agile Data Modeling: A Case Study. Journal of Computational Innovation, 2(1).
  26. 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).
  27. Nookala, G. (2021). Automated Data Warehouse Optimization Using Machine Learning Algorithms. Journal of Computational Innovation, 1(1).
  28. 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).
  29. 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).
  30. Immaneni, J. (2021). Using Swarm Intelligence and Graph Databases for Real-Time Fraud Detection. Journal of Computational Innovation, 1(1).
  31. Immaneni, J. (2020). Cloud Migration for Fintech: How Kubernetes Enables Multi-Cloud Success. Innovative Computer Sciences Journal, 6(1).
  32. Boda, V. V. R., & Immaneni, J. (2019). Streamlining FinTech Operations: The Power of SysOps and Smart Automation. Innovative Computer Sciences Journal, 5(1).
  33. Gade, K. R. (2022). Migrations: AWS Cloud Optimization Strategies to Reduce Costs and Improve Performance. MZ Computing Journal, 3(1).
  34. Gade, K. R. (2022). Cloud-Native Architecture: Security Challenges and Best Practices in Cloud-Native Environments. Journal of Computing and Information Technology, 2(1).
  35. Gade, K. R. (2022). Data Catalogs: The Central Hub for Data Discovery and Governance. Innovative Computer Sciences Journal, 8(1).
  36. Gade, K. R. (2022). Data Lakehouses: Combining the Best of Data Lakes and Data Warehouses. Journal of Computational Innovation, 2(1).
  37. Muneer Ahmed Salamkar. Next-Generation Data Warehousing: Innovations in Cloud-Native Data Warehouses and the Rise of Serverless Architectures. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Apr. 2019
  38. Muneer Ahmed Salamkar. Real-Time Data Processing: A Deep Dive into Frameworks Like Apache Kafka and Apache Pulsar. Distributed Learning and Broad Applications in Scientific Research, vol. 5, July 2019
  39. Muneer Ahmed Salamkar, and Karthik Allam. “Data Lakes Vs. Data Warehouses: Comparative Analysis on When to Use Each, With Case Studies Illustrating Successful Implementations”. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Sept. 2019
  40. Muneer Ahmed Salamkar. Data Modeling Best Practices: Techniques for Designing Adaptable Schemas That Enhance Performance and Usability. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Dec. 2019
  41. Naresh Dulam, et al. “Kubernetes Operators: Automating Database Management in Big Data Systems”. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Jan. 2019
  42. Naresh Dulam, and Karthik Allam. “Snowflake Innovations: Expanding Beyond Data Warehousing ”. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Apr. 2019
  43. Naresh Dulam, and Venkataramana Gosukonda. “AI in Healthcare: Big Data and Machine Learning Applications ”. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Aug. 2019
  44. Naresh Dulam. “Real-Time Machine Learning: How Streaming Platforms Power AI Models ”. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Sept. 2019
  45. Thumburu, S. K. R. (2021). Performance Analysis of Data Exchange Protocols in Cloud Environments. MZ Computing Journal, 2(2).
  46. Thumburu, S. K. R. (2021). Transitioning to Cloud-Based EDI: A Migration Framework, Journal of Innovative Technologies, 4(1).
  47. Thumburu, S. K. R. (2021). Integrating Blockchain Technology into EDI for Enhanced Data Security and Transparency. MZ Computing Journal, 2(1).
  48. Thumburu, S. K. R. (2020). Exploring the Impact of JSON and XML on EDI Data Formats. Innovative Computer Sciences Journal, 6(1).
  49. Thumburu, S. K. R. (2020). Large Scale Migrations: Lessons Learned from EDI Projects. Journal of Innovative Technologies, 3(1).
  50. Sarbaree Mishra, et al. Improving the ETL Process through Declarative Transformation Languages. Distributed Learning and Broad Applications in Scientific Research, vol. 5, June 2019
  51. Sarbaree Mishra. A Novel Weight Normalization Technique to Improve Generative Adversarial Network Training. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Sept. 2019
  52. Sarbaree Mishra. “Moving Data Warehousing and Analytics to the Cloud to Improve Scalability, Performance and Cost-Efficiency”. Distributed Learning and Broad Applications in Scientific Research, vol. 6, Feb. 2020
  53. Sarbaree Mishra, et al. “Training AI Models on Sensitive Data - the Federated Learning Approach”. Distributed Learning and Broad Applications in Scientific Research, vol. 6, Apr. 2020
  54. Sarbaree Mishra. “Automating the Data Integration and ETL Pipelines through Machine Learning to Handle Massive Datasets in the Enterprise”. Distributed Learning and Broad Applications in Scientific Research, vol. 6, June 2020
  55. Komandla, V. Enhancing Product Development through Continuous Feedback Integration “Vineela Komandla”.
  56. Komandla, V. Enhancing Security and Growth: Evaluating Password Vault Solutions for Fintech Companies.
  57. Komandla, V. Strategic Feature Prioritization: Maximizing Value through User-Centric Roadmaps.
  58. Komandla, V. Enhancing Security and Fraud Prevention in Fintech: Comprehensive Strategies for Secure Online Account Opening.
  59. Komandla, Vineela. "Effective Onboarding and Engagement of New Customers: Personalized Strategies for Success." Available at SSRN 4983100 (2019).

Similar Articles

You may also start an advanced similarity search for this article.