Current Methods to Combating Insurance Fraud
https://doi.org/10.26794/2408-9303-2024-11-5-93-103
Abstract
This research examines the impact of fraud prevention measures on organizational and economic relationships in the Russian insurance sector. The purpose of the work is to consider the major methods of fraud prevention in insurance in the Russian Federation. The research methodology comprises the following methods: economical — the use’s analysis of analytical implements in State Farm; analytical — the personnel’s study training system of insurance companies and cooperation with law enforcement agencies; generalization method — systematization of scientific knowledge on the study of analytical instruments in insurance; comparison method — the study of machine learning in insurance and the possibilities of using artificial intelligence. The paper reviews analytical instruments and technologies that improve the efficiency of the internal control and monitoring system. The author gives examples of how these tools and technologies are used in insurance companies. This paper emphasizes the benefits of training insurance company staff and provides examples of effective training programs. Combating fraud in the insurance industry involves creating joint working groups, collaborating with anti-fraud associations, and using shared information platforms. The author provided statistics for all issues under study. Investing in machine learning and AI is crucial for insurance companies to enhance data analysis and fraud detection. Expanding educational programs for employees is essential for the detection and prevention of fraud. Insurance companies and law enforcement should routinely collaborate and share information.
About the Author
E. Yu. Sidorova
Financial University
Russian Federation
Russian Federation
Elena Yu. Sidorova — Dr. Sci. (Econ.), Assoc. Prof., Prof. of the Department of Taxes and Tax Administration
Moscow
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Review
For citations:
Sidorova E.Yu. Current Methods to Combating Insurance Fraud. Accounting. Analysis. Auditing. 2024;11(5):93-103. (In Russ.) https://doi.org/10.26794/2408-9303-2024-11-5-93-103
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