Abstract: A system and method for insurance loss ratio forecasting, which utilizes faster feature reduction by blending traditional statistical method and feature importance, and applying a Boruta algorithm for further feature reduction. Final feature selection is achieved by creating a balance between Light GBM model feature importance and coverage rate. These processes are all completely automated. Faster hyperparameter tuning is achieved by applying a randomized search algorithm. In the out-of-time sample dataset and production sample dataset for an insurance loss ratio forecast, faster segmentation is conducted by applying unsupervised ML, using cosine similarity. The system is a significant technical improvement, which requires uniquely critical computer implementation and ensures that the models are stable for users, across different samples of data, without extensive fine tuning and no manual searches.