Leveraging AI for Improved Insurance Product Development

Authors

  • Dr. Janez Križaj Professor of Computer Science, University of Maribor (UM) Author

Keywords:

AI, Insurance

Abstract

The insurance industry has witnessed a growing interest in innovations operating within the sphere of artificial intelligence. Although the importance of AI in different areas of insurance is significant, its application in insurance product development appeals to us to a great extent. The rise of AI has brought about changes in terms of speed, efficiency of calculation, decision-making processes, and most importantly, the paradigm of the insurance industry from product orientation to customer orientation, ultimately bringing about innovation.

The change started with the advent of machine learning algorithms, especially deep learning, in the insurance product sector. These algorithms provide an opportunity for the insurance industry to examine a substantial, heterogeneous data collection. As a result, they are perceived as sophisticated instruments that not only identify patterns in massive quantities of raw data but also recognize the real requirements, expectations, conduct, fitness, and disposition of the insured, prospects, and corporate clients in light of various practices, alternatives, technologies, and strategies in insurance. Artificial intelligence embodies machine learning and deep learning, as well as other significant transformers such as robo-advisory, data analysis, and blockchain operations. The objective of the present essay is: i. To map the transformation made by AI towards product design and delivery ii. To understand the market requirements of product design and delivery in the insurance sector iii. To study the realization of market demand, AI in product design and development in European insurance. iv. Recommend strategies and takeaways for practitioners in Europe regarding AI usage in decision-making for insurance product development. In collecting the information for the building of our theoretical discussion, we adopt a multidisciplinary approach.

Downloads

Download data is not yet available.

References

S. Kumari, “AI-Driven Cloud Transformation for Product Management: Optimizing Resource Allocation, Cost Management, and Market Adaptation in Digital Products ”, IoT and Edge Comp. J, vol. 2, no. 1, pp. 29–54, Jun. 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.

J. Singh, “The Future of Autonomous Driving: Vision-Based Systems vs. LiDAR and the Benefits of Combining Both for Fully Autonomous Vehicles ”, J. of Artificial Int. Research and App., vol. 1, no. 2, pp. 333–376, Jul. 2021

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.

Ahmed Qureshi, Hamza, et al. “The Promising Role of Artificial Intelligence in Navigating Lung Cancer Prognosis.” International Journal for Multidisciplinary Research, vol. 6, no. 4, 14 Aug. 2024, pp. 1–21.

Singh, Jaswinder. "Deepfakes: The Threat to Data Authenticity and Public Trust in the Age of AI-Driven Manipulation of Visual and Audio Content." Journal of AI-Assisted Scientific Discovery 2.1 (2022): 428-467.

Tamanampudi, Venkata Mohit. "Autonomous AI Agents for Continuous Deployment Pipelines: Using Machine Learning for Automated Code Testing and Release Management in DevOps." Australian Journal of Machine Learning Research & Applications 3.1 (2023): 557-600.

Downloads

Published

21-12-2023

How to Cite

[1]
D. J. Križaj, “Leveraging AI for Improved Insurance Product Development”, Distrib Learn Broad Appl Sci Res, vol. 9, pp. 438–450, Dec. 2023, Accessed: Nov. 14, 2024. [Online]. Available: https://dlabi.org/index.php/journal/article/view/179

Similar Articles

11-20 of 93

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