Publicação
Adversarial generative forecasting of daily Fraud Amount for sparse transaction time series
| datacite.subject.fos | Ciências Sociais::Economia e Gestão | |
| dc.contributor.advisor | Xufre, Patrícia | |
| dc.contributor.author | Mueller, Moritz | |
| dc.date.accessioned | 2026-05-29T12:31:08Z | |
| dc.date.available | 2026-05-29T12:31:08Z | |
| dc.date.issued | 2026-01-21 | |
| dc.date.submitted | 2025-12-17 | |
| dc.description.abstract | 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. | eng |
| dc.identifier.tid | 204242606 | |
| dc.identifier.uri | http://hdl.handle.net/10362/203583 | |
| dc.language.iso | eng | |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | Fraud forecasting | |
| dc.subject | Time series | |
| dc.subject | XGBoost | |
| dc.subject | Generative adversarial neural network | |
| dc.subject | Transfer learning | |
| dc.subject | Value at risk | |
| dc.subject | Quantile regression | |
| dc.subject | Extreme value theory | |
| dc.title | Adversarial generative forecasting of daily Fraud Amount for sparse transaction time series | eng |
| dc.type | master thesis | |
| dspace.entity.type | Publication | |
| thesis.degree.name | A Work Project, presented as part of the requirements for the Award of a Master’s degree in Business Analytics from the Nova School of Business and Economics |