Analysis of Different Insurance Policies Using Machine Learning Algorithms
Amitesh Das, Nitin S. Patil, Subhadip Goswami · 2026
In the beginning, AI and ML have totally transformed the insurance industry, including how insurance firms draft policies and communicate with customers. Although it requires a lot of work, time, and paper, insurance underwriting offers a comprehensive risk evaluation. However, with the advent of AI and ML, which have made such a process more accurate, orientated towards customers, and economical, all of that is obsolete. The capacity of AI and ML to precisely predict what will happen in the future and what is already happening is without a doubt the most substantial change that the insurance industry will experience. In the field of insurance premium computations, which depend on the majority of historical data and major risk classifications, this is quite helpful. But as a result of AI and ML, the insurers can now customize those predictions to include a wide range of customer-specific data points. One-on-one direct connection between consumers and insurers that is more proactive and responsive is made possible by technological integration into operations. Faster methods are also made possible by the application of AI and ML in the areas of improved claims handling rate, policy underwriting speed, and customer inquiry management. By automating operations that lacked strategic significance, the technology allowed the insurance businesses to spend more time on client relationship management and strategic decision-making. The study estimated the insurance premium using a regression approach and an artificial intelligence network model. The model was created on the basis of some parameters complementing the personality of the person-the age, health condition, occupation, and lifestyle of an individual. Depending on all these parameters as inputs in the AI model, the authors were able to forecast the likely costs of insuring each individual. The application of regression is quite suited to ANNs as it had no difficulty in emulating intricate, nonlinear relationships between input variables and the expected outcome in this instance-the insurance premium. Utilizing sophisticated models, like the regression-based ANN in this study, points to how such technologies can be used to refine insurance processes and provide customers with improved, customized services. The insurance sector will likely see a more intensive boost in the development, pricing, and distribution of insurance products with the progression of AI and ML.