AI for Predictive Underwriting in P&C Insurance
Keywords:
Insurance technology, predictive analytics, underwriting automation, property and casualty insurance, machine learning in insurance, customer segmentationAbstract
Artificial Intelligence & machine learning are transforming the P&C insurance industry's underwriting, substituting data-driven decision-making for human procedures. Accuracy & scalability were limited by the inefficiencies, biases, & mistakes that plagued traditional underwriting. Predictive underwriting based on the AI improves accuracy & efficiency by analyzing unstructured & structured information to find impossible risk patterns for humans to detect. Automating guarantees consistency, expedites decision-making, & lowers expenses. Personalized policies that can increase the consumer happiness & loyalty are made possible by adaptive models that can react to changing risks. Artificial Intelligence enhancing risk assessment, pricing, & operating while promoting industrial innovation, not with standing obstacles includes data privacy, transparency, & regulatory compliance. AI-driven underwriting boosts effectiveness & competitiveness by establishing a client-centric ecosystem with customizing options, influencing the future of P&C insurance through increasing consumer trust, accuracy, & responsiveness.
References
1. Sahu, M. K. (2019). Machine Learning Algorithms for Automated Underwriting in Insurance: Techniques, Tools, and Real-World Applications. Distributed Learning & Broad Applications in Scientific Research, 5, 286-326.
2. Rajagopal, S. K. P. (2020). A Review on Artificial Intelligence in Property and Casualty Insurance. International Journal of Innovative Research in Science, Engineering and Technology (IJIRSET), 9(7), 5251-5254.
3. Sanwal, R. (2021). Impact of Artificial Intelligence on the Insurance Industry. In Applications of Artificial Intelligence in Business and Finance (pp. 203-219). Apple Academic Press.
4. Dey, A. J., & Sarma, H. K. D. (2022). A Survey on application of machine learning in property and casualty insurance. In Contemporary Issues in Communication, Cloud and Big Data Analytics: Proceedings of CCB 2020 (pp. 307-314). Springer Singapore.
5. Kasaraneni, B. P. (2019). Advanced Artificial Intelligence Techniques for Predictive Analytics in Life Insurance: Enhancing Risk Assessment and Pricing Accuracy. Distributed Learning and Broad Applications in Scientific Research, 5, 547-588.
6. Kumar, N., Srivastava, J. D., & Bisht, H. (2019). Artificial intelligence in insurance sector. Journal of the Gujarat Research society, 21(7), 79-91.
7. Nimmagadda, V. S. P. (2020). AI-Powered Risk Assessment Models in Property and Casualty Insurance: Techniques, Applications, and Real-World Case Studies. Distributed Learning and Broad Applications in Scientific Research, 6, 194-226.
8. Quang, D. X. (2017). Predictive Underwriting vs Traditional Underwriting with Data Science approach.
9. Akkor, D. G., & Ozyukse, S. (2020). The effects of new technologies on the insurance sector: a proposition for underwriting qualifications for the future. Eurasian Journal of Business and Management, 8(1), 36-50.
10. Larzelere, S. P. (2021). The Cognitive and Emotional Reactions of Commercial Casualty Insurance Underwriters to the Use of Predictive Analytics (Doctoral dissertation, University of Pennsylvania).
11. Krishnakanthan, K., McElhaney, D., Milinkovich, N., & Pradhan, A. (2021). How top tech trends will transform insurance. McKinsey & Company, 9(1), 24-26.
12. Isa, U. A., Fernando, A., & Fernando, T. G. I. (2022). Artificial Intelligence for Insurance Industry: A Survey.
13. Balasubramanian, R., Libarikian, A., & McElhaney, D. (2018). Insurance 2030—The impact of AI on the future of insurance. McKinsey & Company.
14. Dabbugudi, M. (2022). Artificial Intelligence on Property and Casualty Insurance. European Journal of Electrical Engineering and Computer Science, 6(6), 26-30.
15. Paul, C., Gauthier, E., Aurégann, L., & P&C, A. G. (2017). P&C Reinsurance modeling.
16. Katari, A. Conflict Resolution Strategies in Financial Data Replication Systems.
17. Katari, A., & Rallabhandi, R. S. DELTA LAKE IN FINTECH: ENHANCING DATA LAKE RELIABILITY WITH ACID TRANSACTIONS.
18. Katari, A. (2019). Real-Time Data Replication in Fintech: Technologies and Best Practices. Innovative Computer Sciences Journal, 5(1).
19. Katari, A. (2019). ETL for Real-Time Financial Analytics: Architectures and Challenges. Innovative Computer Sciences Journal, 5(1).
20. Katari, A. (2019). Data Quality Management in Financial ETL Processes: Techniques and Best Practices. Innovative Computer Sciences Journal, 5(1).
21. Babulal Shaik. Network Isolation Techniques in Multi-Tenant EKS Clusters. Distributed Learning and Broad Applications in Scientific Research, vol. 6, July 2020
22. 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
23. 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
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). Data Catalogs: The Central Hub for Data Discovery and Governance. Innovative Computer Sciences Journal, 8(1).
34. Gade, K. R. (2022). Data Lakehouses: Combining the Best of Data Lakes and Data Warehouses. Journal of Computational Innovation, 2(1).
35. Gade, K. R. (2021). Cost Optimization Strategies for Cloud Migrations. MZ Computing Journal, 2(2).
36. Gade, K. R. (2021). Cloud Migration: Challenges and Best Practices for Migrating Legacy Systems to the Cloud. Innovative Engineering Sciences Journal, 1(1).
37. Gade, K. R. (2021). Data Analytics: Data Democratization and Self-Service Analytics Platforms Empowering Everyone with Data. MZ Computing Journal, 2(1).
38. Muneer Ahmed Salamkar. ETL Vs ELT: A Comprehensive Exploration of Both Methodologies, Including Real-World Applications and Trade-Offs. Distributed Learning and Broad Applications in Scientific Research, vol. 5, Mar. 2019
39. 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
40. 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
41. 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
42. 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
43. Naresh Dulam, et al. “The AI Cloud Race: How AWS, Google, and Azure Are Competing for AI Dominance ”. Journal of AI-Assisted Scientific Discovery, vol. 1, no. 2, Dec. 2021, pp. 304-28
44. Naresh Dulam, et al. “Kubernetes Operators for AI ML: Simplifying Machine Learning Workflows”. African Journal of Artificial Intelligence and Sustainable Development, vol. 1, no. 1, June 2021, pp. 265-8
45. Naresh Dulam, et al. “Data Mesh in Action: Case Studies from Leading Enterprises”. Journal of Artificial Intelligence Research and Applications, vol. 1, no. 2, Dec. 2021, pp. 488-09
46. Naresh Dulam, et al. “Real-Time Analytics on Snowflake: Unleashing the Power of Data Streams”. Journal of Bioinformatics and Artificial Intelligence, vol. 1, no. 2, July 2021, pp. 91-114
47. Naresh Dulam, et al. “Serverless AI: Building Scalable AI Applications Without Infrastructure Overhead ”. Journal of AI-Assisted Scientific Discovery, vol. 2, no. 1, May 2021, pp. 519-42
48. Thumburu, S. K. R. (2022). Post-Migration Analysis: Ensuring EDI System Performance. Journal of Innovative Technologies, 5(1).
49. Thumburu, S. K. R. (2022). Scalable EDI Solutions: Best Practices for Large Enterprises. Innovative Engineering Sciences Journal, 2(1).
50. Thumburu, S. K. R. (2022). Data Integration Strategies in Hybrid Cloud Environments. Innovative Computer Sciences Journal, 8(1).
51. Thumburu, S. K. R. (2022). Transforming Legacy EDI Systems: A Comprehensive Migration Guide. Journal of Innovative Technologies, 5(1).
52. Sarbaree Mishra. “Leveraging Cloud Object Storage Mechanisms for Analyzing Massive Datasets”. African Journal of Artificial Intelligence and Sustainable Development, vol. 1, no. 1, Jan. 2021, pp. 286-0
53. Sarbaree Mishra, et al. “A Domain Driven Data Architecture For Improving Data Quality In Distributed Datasets”. Journal of Artificial Intelligence Research and Applications, vol. 1, no. 2, Aug. 2021, pp. 510-31
54. Sarbaree Mishra. “Improving the Data Warehousing Toolkit through Low-Code No-Code”. Journal of Bioinformatics and Artificial Intelligence, vol. 1, no. 2, Oct. 2021, pp. 115-37
55. Sarbaree Mishra, and Jeevan Manda. “Incorporating Real-Time Data Pipelines Using Snowflake and Dbt”. Journal of AI-Assisted Scientific Discovery, vol. 1, no. 1, Mar. 2021, pp. 205-2
56. Sarbaree Mishra. “Building A Chatbot For The Enterprise Using Transformer Models And Self-Attention Mechanisms”. Australian Journal of Machine Learning Research & Applications, vol. 1, no. 1, May 2021, pp. 318-40
57. Komandla, V. Enhancing Security and Fraud Prevention in Fintech: Comprehensive Strategies for Secure Online Account Opening.
58. Komandla, Vineela. "Effective Onboarding and Engagement of New Customers: Personalized Strategies for Success." Available at SSRN 4983100 (2019).
59. Komandla, V. Transforming Financial Interactions: Best Practices for Mobile Banking App Design and Functionality to Boost User Engagement and Satisfaction.
60. Komandla, Vineela. "Transforming Financial Interactions: Best Practices for Mobile Banking App Design and Functionality to Boost User Engagement and Satisfaction." Available at SSRN 4983012 (2018).
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
