Vol. 4 No. 2 (2024): Hong Kong Journal of AI and Medicine
Articles

AI-Based Predictive Analytics for Autonomous Vehicle Insurance Risk Assessment

Dr. Benjamin Jones
Professor of Cybersecurity, Edith Cowan University, Australia

Published 23-10-2024

Keywords

  • AI-Based,
  • Predictive Analytics,
  • Autonomous Vehicle Insurance

How to Cite

[1]
D. B. Jones, “AI-Based Predictive Analytics for Autonomous Vehicle Insurance Risk Assessment”, Hong Kong J. of AI and Med., vol. 4, no. 2, pp. 60–80, Oct. 2024, Accessed: Nov. 21, 2024. [Online]. Available: https://hongkongscipub.com/index.php/hkjaim/article/view/83

Abstract

The development of autonomous vehicles (AVs), the cutting-edge technological advancement, is gaining enthusiastic acceptance from the community at large. Autonomous vehicles will disrupt existing technologies to a significant extent. In the context of vehicle insurance, the increasing number of road mishaps and traffic density necessitates autonomous vehicle insurance. One of the main needs before insuring autonomous vehicles is accurate risk assessment. Risk assessment in autonomous vehicle insurance will greatly benefit stakeholders, including insurance companies, by identifying which policies to underwrite and which to decline. The trustworthiness of decision-making is based on the same factors. Risks and unwanted incidents occur during the underwriting of insurance. By analyzing scenes without human intervention, automotive companies use a predictive algorithm based on artificial intelligence to transport passengers efficiently from one location to another. The same principle can be applied to the underwriting of AV insurance, saving time and resources. The evolution of autonomous transportation systems is on the rise. With the adoption of autonomous vehicles, the concept and framework of vehicle insurance policies have undergone a significant shift. Information about the various business components necessitates an intelligent and accurate risk assessment system. Risks associated with advanced and autonomous vehicle technologies can be minimized through the precision of risk assessment decisions. To accomplish this, machine learning and artificial intelligence are implemented in this study using an algorithm for predictive analytics to categorize and assess the risk of autonomous vehicles. This essay focuses on the concept of returning autonomous vehicles equipped with AI predictive analytics as well as the subsequent process of assessing the risks associated with the driving of AVs alongside case studies. Given the demand for autonomous vehicle insurance, we concentrate on utilizing AI and machine learning algorithms in a practical setting to decrease costs and improve the precision of insurance risk assessments.

Downloads

Download data is not yet available.

References

  1. 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.
  2. Pal, Dheeraj Kumar Dukhiram, et al. "AI-Assisted Project Management: Enhancing Decision-Making and Forecasting." Journal of Artificial Intelligence Research 3.2 (2023): 146-171.
  3. Kodete, Chandra Shikhi, et al. "Determining the efficacy of machine learning strategies in quelling cyber security threats: Evidence from selected literatures." Asian Journal of Research in Computer Science 17.8 (2024): 24-33.
  4. Singh, Jaswinder. "The Rise of Synthetic Data: Enhancing AI and Machine Learning Model Training to Address Data Scarcity and Mitigate Privacy Risks." Journal of Artificial Intelligence Research and Applications 1.2 (2021): 292-332.
  5. Alluri, Venkat Rama Raju, et al. "Serverless Computing for DevOps: Practical Use Cases and Performance Analysis." Distributed Learning and Broad Applications in Scientific Research 4 (2018): 158-180.
  6. Machireddy, Jeshwanth Reddy. "Revolutionizing Claims Processing in the Healthcare Industry: The Expanding Role of Automation and AI." Hong Kong Journal of AI and Medicine 2.1 (2022): 10-36.
  7. 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.
  8. J. Singh, “How RAG Models are Revolutionizing Question-Answering Systems: Advancing Healthcare, Legal, and Customer Support Domains”, Distrib Learn Broad Appl Sci Res, vol. 5, pp. 850–866, Jul. 2019
  9. S. Kumari, “AI-Enhanced Mobile Platform Optimization: Leveraging Machine Learning for Predictive Maintenance, Performance Tuning, and Security Hardening ”, Cybersecurity & Net. Def. Research, vol. 4, no. 1, pp. 29–49, Aug. 2024
  10. Tamanampudi, Venkata Mohit. "Leveraging Machine Learning for Dynamic Resource Allocation in DevOps: A Scalable Approach to Managing Microservices Architectures." Journal of Science & Technology 1.1 (2020): 709-748.