Application of machine learning and data visualization techniques for decision support in the insurance sector

The insurance industry has a giant role in the sustainable economic growth of any country. With an increase in the number of insurance buyers, it has become an absolute necessity for an insurance company to have a detailed claim analysis system in place. Claim Analysis is performed by insurance companies to distinguish between fraudulent and genuine claims. Apart from that, Claim Analysis can also be used to understand the client strata in a much better way and implement the results further during the underwriting and acceptance/denial stage of policy enrollment. The main objective of this research work is to identify meaningful and decisive factors for claim filing and acceptance in a learning context through exploratory data analysis (EDA) and feature selection techniques. Also, machine learning algorithms are applied to the datasets and are evaluated using performance metrics.