Business predictive analysis from business insurance data using business strategic planning techniques

Abstract

ABSTRACT Business analytics is an effective means of examining business performance, employee skills, and working conditions as part of business growth strategy. This study uses statistical analysis to derive the necessary information to optimize performance in insurance businesses. The collected customer-related information, services, identities, and other valuable data are stored in the blockchain and processed using the probit linear regression approach (PLR). The data is further examined using dynamic Monte Carlo statistical analysis, which introduces techniques that aid product-based predictive analysis as firms seek to increase market share in a given environment in the minimum time. Finally, system performance is evaluated using experimental analysis on the Kaggle Sample Insurance Claim Prediction Dataset. The MATLAB-based developed system efficiency is quantified in terms of prediction rate, accuracy, error rate, and correlation metrics.