Leveraging Machine Learning Algorithms for Risk Assessment in Auto Insurance

Authors

Keywords:

Machine Learning, Auto Insurance, Risk Assessment, Predictive Modeling, Claims Frequency, Severity Estimation, Fraud Detection, Data Analysis, Optimization, Insurance Operations

Abstract

This paper delves into the burgeoning domain of leveraging machine learning (ML) algorithms for risk assessment in the auto insurance sector. It investigates the application of diverse ML techniques for predictive modeling, encompassing claims frequency, severity estimation, and fraud detection. By analyzing vast datasets, ML algorithms offer promising avenues for enhancing risk assessment accuracy, thereby optimizing insurance operations. This research elucidates the theoretical underpinnings of ML algorithms employed in auto insurance risk assessment and evaluates their efficacy through empirical case studies. Through comprehensive analysis and synthesis, this paper contributes to advancing the understanding of ML's role in revolutionizing risk assessment methodologies within the auto insurance industry.

References

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Published

14-04-2021

How to Cite

[1]
P. Zanke and D. Sontakke, “Leveraging Machine Learning Algorithms for Risk Assessment in Auto Insurance”, J. of Art. Int. Research, vol. 1, no. 1, pp. 21–39, Apr. 2021.

Issue

Vol. 1 No. 1 (2021): Journal of Artificial Intelligence Research

Section

Articles

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