IMPROVING THE INSURANCE INDUSTRY: A CONCEPTUAL FRAMEWORK FOR APPLYING MACHINE LEARNING BASED ON A SYSTEMATIC LITERATURE REVIEW

Nikola Medan; Dejana Kresović

doi:10.5937/ekonsig2502051M

Authors

Nikola Medan Faculty of Organizational Sciences, University of Belgrade Author
Dejana Kresović Securities Commission Author

DOI:

https://doi.org/10.5937/ekonsig2502051M

Keywords:

machine learning, insurance, learning algorithms

Abstract

The insurance industry is undergoing a significant transformation driven by advancements in technology, particularly machine learning. As insurers seek to enhance operational efficiency, risk assessment, and customer experience, machine learning offers promising applications across various domains, such as underwriting, claims processing, and fraud detection. Despite the potential of machine learning, its integration into traditional insurance practices faces numerous challenges, including data quality, regulatory concerns, and organizational readiness. The aim of this paper is to examine the possibilities and characteristics of the application of machine learning in insurance, in order to determine the machine learning approach that is most often used and that provides the best results. Drawing on insights from systematic literature reviews, the framework will provide a comprehensive understanding of how machine learning can reshape insurance practices. By exploring these aspects, this paper contributes to a more structured and informed approach to implementing machine learning in the insurance industry.

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