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Adversarial generative forecasting of daily Fraud Amount for sparse transaction time series
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In collaboration with SIBS, this project forecasts daily accepted fraud amounts in e-commerce transactions to support proactive risk management. Utilizing a dataset ofover 166 million transactions (2023–2024), we engineered behavioral features to benchmark multiple machine learning models. XGBoost was the champion model, achievinga 16.56% MAPE, but struggling during volatility spikes. To improve reliability duringspike periods, we investigated three complementary strategies: GAN-based data augmentation to increase exposure to synthetic high-fraud scenarios, quantile-based forecasting to model the upper tail of the distribution, and transfer learning approaches that adapt large pre-trained time-series models to our use case.
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Palavras-chave
Fraud forecasting Time series XGBoost Generative adversarial neural network Transfer learning Value at risk Quantile regression Extreme value theory