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.

Abstract: A method that includes (a) receiving a training dataset, a testing dataset, a number of iterations, and a parameter space of possible parameter values that define a base model, (b) for the number of iterations, performing a parametric search process that produces a report that includes information concerning a plurality of machine learning models, where the parametric search process includes (i) generating a Bayesian optimized parameter space with an option to validate through Stratified Kfold cross validation, where an optimized parameter set includes training data from the training dataset, and testing data from the testing dataset, (ii) running the base model with the final optimized parameter set, thus yielding model results for the plurality of machine learning models, (iii) calculating Kolmogorov-Smirnov (KS) statistics for the model results, and (iv) saving the model results and the KS statistics to the report, and (c) sending the report to a user device.