Authors: Jacob Atkinson
Mentors: Vinodh Chellamuthu
Insitution: Utah Tech University
Insurance is a crucial part of economies worldwide, paying billions of dollars in claims yearly. Insurance companies need to anticipate future claim liabilities to manage the high volume of claims. This research investigates the use of linear and non-linear machine learning algorithms, including linear regression, ridge, lasso, elastic net, Decision Tree Regressor, Random Forest Regressor, and Gradient Boosting Regressor, to predict auto insurance claim amounts. The performance of each model is assessed using various metrics, such as mean squared errors, mean absolute errors, and R-squared. An optimized model will also be used to estimate the future financial impact.