I created a claims forecasting system for an insurance company. It could determine the probability of making insurance claims using various risk factors. The model could also predict and thus prevent possible future claims. First, I collected a large dataset containing policyholder information, claim types, amounts, age groups, location histories, driving records, etc. I then looked at different variables that could affect the frequency and severity of claims, paying attention only to those significantly impacting risk management. With these statistics, I designed a machine learning-based forecasting model. It could identify patterns among different factors through correlation analysis. For instance, this system could examine fresh customer information and determine the probability and related costs. Premium setting is one area that benefited from accurate risk predictions since it allowed charging appropriate rates depending on the danger levels.