Implications for Big Data Analytics on Claims Fraud Management in Insurance Sector

Abstract

Purpose: Because of the enormous financial burden that insurance fraud places on businesses, executives are moving quickly to implement big data analytics and other forms of cutting-edge technology in order to combat the issue. The purpose of the study is to assess the implications for Big data analytics on claims fraud management in insurance sector.
 
 Methodology: This was accomplished through the use of a desktop literature review. The use of Google Scholar was utilized in order to locate seminal references and journal articles that were pertinent to the study. In order to meet the inclusion criteria, the papers had to be no more than ten years old.
 
 Findings: The study concludes that Big Data Analytics in the insurance industry is becoming a promising field for gaining insight from very large data sets, enhancing outcomes, and lowering costs. It has tremendous potential, but there are still obstacles to overcome. The findings demonstrated that digital fraud detection had a positive and significant impact on insurers' underwriting procedures.
 Unique contribution to theory, practice and policy: The research suggests that insurers should always strive to automate their claim processes. In addition, the study suggests that insurers implement elements of constructing digital insurance control mechanisms. Before incorporating new technologies and analytical tools, they recommend organizations to conduct a thorough cost-benefit analysis and scenario planning to address unintended outcomes.

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