Large scale transactional platforms processing millions of daily events require high-performance algorithmic fraud intervention. However, deploying models that block bad actors introduces a structural blind spot: the censorship of positive labels needed to train the next generation of models. To generate unbiased data, systems typically rely on a holdout group, a subsample where the model doesn't act. This creates an operational tradeoff: a large holdout caps the model's ability to intervene, while a small holdout reduces the sample size of the training pipeline, especially when the labels are heavily unbalanced.
This talk details the methodology developed to mitigate this reject inference using LightGBM and Optuna. We will walk through the mathematical theory of Inverse Propensity Weighting (IPW) to reconstruct missing data, and examine why applying standard IPW directly to GBDTs often causes gradient explosion and overfitting. To bridge the gap between statistical theory and tree-based mechanics, we will introduce Asymmetrical IPW: a hybrid weighting architecture that stabilizes tree gradients using optimizable, asymmetrical weights. This approach yielded a model recovering 99% of the benchmark's partial AUC (@20% FPR), where the benchmark is a model trained on fully uncensored data.
