Implementing Machine Learning Methods in Insurance Industry Practice

Abstract

The article examines contemporary practices of implementing machine learning methods within the insurance industry. It analyses key applications of machine learning, including risk assessment and forecasting, claims processing automation, fraud prevention, and the personalisation of insurance products and pricing. We focus particularly on the adoption of cluster analysis for customer segmentation. Drawing on case studies from leading Russian and international insurers, the study demonstrates that machine learning adoption reduces operational costs, enhances risk assessment accuracy, and minimises human-factor errors. The findings indicate that machine learning implementation has become a critical competitive differentiator in an increasingly digitalised market, where growing data volumes necessitate advanced analytical capabilities – delivering not only significant cost efficiencies but also improved employee productivity by automating routine tasks. Future integration of machine learning algorithms is expected to substantially reduce processing times for policyholders through automated claims submissions. However, successful deployment requires upskilling employees and access to large datasets, the latter often proving challenging for smaller insurers. The article also addresses data privacy concerns and regulatory considerations in this domain.