AI-Enhanced Risk Assessment Models in Insurance
Cover
PDF

Keywords

AI-Enhanced
Risk Assessment
Insurance

How to Cite

[1]
D. M. Robnik-Šikonja, “AI-Enhanced Risk Assessment Models in Insurance”, Journal of Bioinformatics and Artificial Intelligence, vol. 3, no. 2, pp. 213–229, Nov. 2023, Accessed: Nov. 21, 2024. [Online]. Available: https://biotechjournal.org/index.php/jbai/article/view/114

Abstract

Insurance, as a social and economic institution, has often been associated with uncertainty, risk, and the unpredictability of the future. As a part of this, the companies and individuals operating within this highly dynamic sector face an array of risks ranging from the type of product or service offered to operational ones such as customer default. To ensure long-term profitability and performance, better risk management improves decision processes, beneficially influencing an entire business operating ecosystem. Traditionally, the industry has attempted to adopt three different methodologies for constructing state-of-the-art business intelligence and data-driven risk assessment models. Namely, these are: expert systems using rule-based techniques, statistical methods, and optimization models. However, these conventional methodologies have proven to be high in effectiveness but expensive in terms of adaptability and efficiency, which makes their applicability complex in modern environments.

PDF

References

S. Kumari, “Cybersecurity in Digital Transformation: Using AI to Automate Threat Detection and Response in Multi-Cloud Infrastructures ”, J. Computational Intel. & Robotics, vol. 2, no. 2, pp. 9–27, Aug. 2022

Tamanampudi, Venkata Mohit. "Automating CI/CD Pipelines with Machine Learning Algorithms: Optimizing Build and Deployment Processes in DevOps Ecosystems." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 810-849.

Machireddy, Jeshwanth Reddy. "Data-Driven Insights: Analyzing the Effects of Underutilized HRAs and HSAs on Healthcare Spending and Insurance Efficiency." Journal of Bioinformatics and Artificial Intelligence 1.1 (2021): 450-470.

Singh, Jaswinder. "Social Data Engineering: Leveraging User-Generated Content for Advanced Decision-Making and Predictive Analytics in Business and Public Policy." Distributed Learning and Broad Applications in Scientific Research 6 (2020): 392-418.

Tamanampudi, Venkata Mohit. "AI and DevOps: Enhancing Pipeline Automation with Deep Learning Models for Predictive Resource Scaling and Fault Tolerance." Distributed Learning and Broad Applications in Scientific Research 7 (2021): 38-77.

J. Singh, “Combining Machine Learning and RAG Models for Enhanced Data Retrieval: Applications in Search Engines, Enterprise Data Systems, and Recommendations ”, J. Computational Intel. & Robotics, vol. 3, no. 1, pp. 163–204, Mar. 2023.

Tamanampudi, Venkata Mohit. "AI Agents in DevOps: Implementing Autonomous Agents for Self-Healing Systems and Automated Deployment in Cloud Environments." Australian Journal of Machine Learning Research & Applications 3.1 (2023): 507-556.

Downloads

Download data is not yet available.