abstract

Business prediction findings are vital for assessing a company's future financial success in the context of contemporary business practices. Procedures for planning and prediction are particularly crucial for businesses that operate in an uncertain environment. This study provides an illustration of how to plan and forecast business outcomes in the insurance industry when using linear and nonlinear regression to calculate premium trends. It is essential to obtain sufficient assets to cover the risks because of the uncertainty around the claim's incidence and amount. Predicting future premium movements for individual insurance lines is necessary for asset-liability matching, which is the fundamental idea behind the growth and functioning of insurance businesses. This study examines how regression models can help create strategic financial insights and examines the use of regression analysis in financial forecasting, particularly for small enterprises. Small firms find it difficult to use advanced forecasting techniques because they have limited access to large datasets and financial models. To forecast revenue, expenses, profitability, and other financial metrics that are crucial for small firms, a variety of regression techniques, such as logistic, multivariate, and linear regression models, can be modified. The strengths, drawbacks, and implementation strategies of regression analysis are further examined in this study, along with case examples illustrating its real-world uses.