Improving Loss Prediction Accuracy Through Advanced ML Ensembles

The accurate estimation of financial loss is the fundamental objective of modern actuarial science and credit risk management. Traditional parametric models, specifically Generalized Linear Models (GLMs), have historically provided a balance between predictive utility and structural transparency. However, the contemporary landscape of high-dimensional data, characterized by non-linear interactions and structural anomalies such as zero-inflation and heavy tails, necessitates the adoption of advanced computational frameworks. This report provides an exhaustive investigation into the application of machine learning (ML) ensemble techniques to improve loss prediction accuracy. The analysis encompasses a detailed examination of bagging, boosting, and stacked generalization architectures, with a specific focus on their capacity to handle the unique distributional traits of financial loss data. By synthesizing research on gradient boosting libraries—including XGBoost, LightGBM, and CatBoost—this study evaluates the mathematical implementation of Tweedie loss functions and hurdle models. Furthermore, the report explores the integration of hybrid resampling techniques to address class imbalance and the deployment of Explainable Artificial Intelligence (XAI) to reconcile the “black box” nature of ensembles with regulatory requirements. The evidence suggests that multi-tiered stacking and specialized boosting architectures significantly outperform individual learners and traditional regressions, provided that hyperparameter optimization and distributional constraints are rigorously maintained through systematic optimization protocols such as GEM-ITH.