FraudTransformer: Time-Aware GPT for Transaction Fraud Detection
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911235949199360 |
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| author | Aminian, Gholamali Elliott, Andrew Li, Tiger Wong, Timothy Cheuk Hin Dehon, Victor Claude Szpruch, Lukasz Maple, Carsten Read, Christopher Brown, Martin Reinert, Gesine Mamouei, Mo |
| author_facet | Aminian, Gholamali Elliott, Andrew Li, Tiger Wong, Timothy Cheuk Hin Dehon, Victor Claude Szpruch, Lukasz Maple, Carsten Read, Christopher Brown, Martin Reinert, Gesine Mamouei, Mo |
| contents | Detecting payment fraud in real-world banking streams requires models that can exploit both the order of events and the irregular time gaps between them. We introduce FraudTransformer, a sequence model that augments a vanilla GPT-style architecture with (i) a dedicated time encoder that embeds either absolute timestamps or inter-event values, and (ii) a learned positional encoder that preserves relative order. Experiments on a large industrial dataset -- tens of millions of transactions and auxiliary events -- show that FraudTransformer surpasses four strong classical baselines (Logistic Regression, XGBoost and LightGBM) as well as transformer ablations that omit either the time or positional component. On the held-out test set it delivers the highest AUROC and PRAUC. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_23712 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | FraudTransformer: Time-Aware GPT for Transaction Fraud Detection Aminian, Gholamali Elliott, Andrew Li, Tiger Wong, Timothy Cheuk Hin Dehon, Victor Claude Szpruch, Lukasz Maple, Carsten Read, Christopher Brown, Martin Reinert, Gesine Mamouei, Mo Machine Learning Detecting payment fraud in real-world banking streams requires models that can exploit both the order of events and the irregular time gaps between them. We introduce FraudTransformer, a sequence model that augments a vanilla GPT-style architecture with (i) a dedicated time encoder that embeds either absolute timestamps or inter-event values, and (ii) a learned positional encoder that preserves relative order. Experiments on a large industrial dataset -- tens of millions of transactions and auxiliary events -- show that FraudTransformer surpasses four strong classical baselines (Logistic Regression, XGBoost and LightGBM) as well as transformer ablations that omit either the time or positional component. On the held-out test set it delivers the highest AUROC and PRAUC. |
| title | FraudTransformer: Time-Aware GPT for Transaction Fraud Detection |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2509.23712 |