Enhancing Risk Assessment, Decision-Making, and Operational Efficiency: AI and Machine Learning Can Help to Transform Underwriting Practices
Keywords:
Artificial Intelligence, Machine Learning, Financial Services, Big Data, Algorithmic Models, Digital Transformation, Financial TechnologyAbstract
Thanks in great part to AI & ML, the financial services industry, especially underwriting is undergoing a significant change. By improving the operational efficiency, risk assessments & decision-making process optimization, these technologies are changing underwriting. By exposing hitherto invisible trends & the patterns, AI & ML helps insurers to make more accurate decisions by means of actual time analysis of large data sets. More exact risk evaluations made possible by this change help to drive better pricing, tailored insurance, and a faster general process. Moreover, artificial intelligence and machine learning are meant to reduce human prejudices, therefore supporting a more fair and objective underwriting procedure.
References
1. Zanke, P., & Sontakke, D. (2021). Artificial Intelligence Applications in Predictive Underwriting for Commercial Lines Insurance. Advances in Deep Learning Techniques, 1(1), 23-38.
2. Knight, E. (2019). AI and machine learning-based credit underwriting and adverse action under the ECOA. Bus. & Fin. L. Rev., 3, 236.
3. Ashta, A., & Herrmann, H. (2021). Artificial intelligence and fintech: An overview of opportunities and risks for banking, investments, and microfinance. Strategic Change, 30(3), 211-222.
4. Cooper, S. (2019). Insurance and artificial intelligence: Underwriting, claims and litigation. In New Technologies, Artificial Intelligence and Shipping Law in the 21st Century (pp. 178-190). Informa Law from Routledge.
5. Mullangi, K. (2017). Enhancing Financial Performance through AIdriven Predictive Analytics and Reciprocal Symmetry. Asian Accounting and Auditing Advancement, 8(1), 57-66.
6. Sabharwal, C. L. (2018). The rise of machine learning and robo-advisors in banking. IDRBT Journal of Banking Technology, 28.
7. Boodhun, N., & Jayabalan, M. (2018). Risk prediction in life insurance industry using supervised learning algorithms. Complex & Intelligent Systems, 4(2), 145-154.
8. McPhail, L. L., & McPhail, J. E. (2021). Machine learning implications for banking regulation. In The Routledge Handbook of FinTech (pp. 395-415). Routledge.
9. Sivathapandi, P., Paul, D., & Sudharsanam, S. R. (2021). Enhancing Cloud-Native CI/CD Pipelines with AI-Driven Automation and Predictive Analytics. Australian Journal of Machine Learning Research & Applications, 1(1), 226-265.
10. Eling, M., & Lehmann, M. (2018). The impact of digitalization on the insurance value chain and the insurability of risks. The Geneva papers on risk and insurance-issues and practice, 43, 359-396.
11. Nayak, B., Bhattacharyya, S. S., & Kri shnamoorthy, B. (2019). Democratizing health insurance services; accelerating social inclusion through technology policy of health insurance firms. Business Strategy & Development, 2(3), 242-252.
12. Mulyani, I., & Wibowo, A. (2021). Data Quality Assurance and Governance in the Age of Big Data: Strategies, Challenges, and Industry Applications. Emerging Trends in Machine Intelligence and Big Data, 13(7), 49-65.
13. Casino, F., Dasaklis, T. K., & Patsakis, C. (2019). A systematic literature review of blockchain-based applications: Current status, classification and open issues. Telematics and informatics, 36, 55-81.
14. Todri, A., & Papajorgji, P. (2008). Artificial Intelligence Waves In Financial Services Industry: An Evolution Factorial Analysis. RSI J, 63.
15. Turban, E. (2011). Decision support and business intelligence systems. Pearson Education India.
16. Katari, A., Muthsyala, A., & Allam, H. HYBRID CLOUD ARCHITECTURES FOR FINANCIAL DATA LAKES: DESIGN PATTERNS AND USE CASES.
17. Katari, A. Conflict Resolution Strategies in Financial Data Replication Systems.
18. Katari, A., & Rallabhandi, R. S. DELTA LAKE IN FINTECH: ENHANCING DATA LAKE RELIABILITY WITH ACID TRANSACTIONS.
19. Katari, A. (2019). Real-Time Data Replication in Fintech: Technologies and Best Practices. Innovative Computer Sciences Journal, 5(1).
20. Katari, A. (2019). ETL for Real-Time Financial Analytics: Architectures and Challenges. Innovative Computer Sciences Journal, 5(1).
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. (2021). Unified Data Architectures: Blending Data Lake, Data Warehouse, and Data Mart Architectures. MZ Computing Journal, 2(2).
25. Nookala, G. (2021). Automated Data Warehouse Optimization Using Machine Learning Algorithms. Journal of Computational Innovation, 1(1).
26. 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).
27. , G., Gade, K. R., Dulam, N., & Thumburu, S. K. R. (2020). Data Virtualization as an Alternative to Traditional Data Warehousing: Use Cases and Challenges. Innovative Computer Sciences Journal, 6(1).
28. Nookala, G., Gade, K. R., Dulam, N., & Thumburu, S. K. R. (2019). End-to-End Encryption in Enterprise Data Systems: Trends and Implementation Challenges. Innovative Computer Sciences Journal, 5(1).
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. (2021). Cost Optimization Strategies for Cloud Migrations. MZ Computing Journal, 2(2).
34. Gade, K. R. (2021). Cloud Migration: Challenges and Best Practices for Migrating Legacy Systems to the Cloud. Innovative Engineering Sciences Journal, 1(1).
35. Gade, K. R. (2021). Data Analytics: Data Democratization and Self-Service Analytics Platforms Empowering Everyone with Data. MZ Computing Journal, 2(1).
36. Gade, K. R. (2021). Data-Driven Decision Making in a Complex World. Journal of Computational Innovation, 1(1).
37. Gade, K. R. (2021). Migrations: Cloud Migration Strategies, Data Migration Challenges, and Legacy System Modernization. Journal of Computing and Information Technology, 1(1).
38. 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
39. 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
40. 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
41. 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
42. Muneer Ahmed Salamkar, and Jayaram Immaneni. Automated Data Pipeline Creation: Leveraging ML Algorithms to Design and Optimize Data Pipelines. Journal of AI-Assisted Scientific Discovery, vol. 1, no. 1, June 2021, pp. 230-5
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. (2021). The Future of EDI Standards in an API-Driven World. MZ Computing Journal, 2(2).
49. Thumburu, S. K. R. (2021). Optimizing Data Transformation in EDI Workflows. Innovative Computer Sciences Journal, 7(1).
50. Thumburu, S. K. R. (2021). Performance Analysis of Data Exchange Protocols in Cloud Environments. MZ Computing Journal, 2(2).
51. Thumburu, S. K. R. (2021). Transitioning to Cloud-Based EDI: A Migration Framework, Journal of Innovative Technologies, 4(1).
52. Thumburu, S. K. R. (2021). Integrating Blockchain Technology into EDI for Enhanced Data Security and Transparency. MZ Computing Journal, 2(1).
53. Sarbaree Mishra. “The Age of Explainable AI: Improving Trust and Transparency in AI Models”. Journal of AI-Assisted Scientific Discovery, vol. 1, no. 2, Oct. 2021, pp. 212-35
54. Sarbaree Mishra, et al. “A New Pattern for Managing Massive Datasets in the Enterprise through Data Fabric and Data Mesh”. Journal of AI-Assisted Scientific Discovery, vol. 1, no. 2, Dec. 2021, pp. 236-59
55. 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
56. 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
57. 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
58. Komandla, V. Strategic Feature Prioritization: Maximizing Value through User-Centric Roadmaps.
59. Komandla, V. Enhancing Security and Fraud Prevention in Fintech: Comprehensive Strategies for Secure Online Account Opening.
60. Komandla, Vineela. "Effective Onboarding and Engagement of New Customers: Personalized Strategies for Success." Available at SSRN 4983100 (2019).
61. Komandla, V. Transforming Financial Interactions: Best Practices for Mobile Banking App Design and Functionality to Boost User Engagement and Satisfaction.
62. 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).
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
