Data Analytics in Insurance Product Management

Data analytics as a part of insurance product management is revolutionizing the industry because with huge and constantly increasing piles of customer and claims data at their fingertips, insurers can make better decisions and improve many aspects of their operations. This paper discusses how the adaptation of risk models and artificial intelligence models helps insurers to improve evaluation criteria and policy premiums, as well as predict the occurrence of claims with a high degree of certainty. Challenging customer segments can be detected using big data analytics, which helps insurers better serve their clients and gain their trust. In addition, there is also the function of using data analytics to detect frauds hence predictive models detect potential frauds since they identify cer tain patterns. However, the application of data analytics in insurance product management has some difficulties in terms of data quality, data privacy, and human resources to analyze sophisticated data. These challenges demand strong investments in infrastructure for storage, terrible, and processing as well as the recruitment and training of skilled data professionals, together with a solid data governance mentality. This abstract establishes that, in addition to improving the internal processes within insurance organizations, data analytics also increases market competitiveness, innovation, and customer focus in insurance products.