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Reconstructing Fraudulent Strategies in Online Payments via Interpretable Inverse Reinforcement Learning — arXiv:2611.04819

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.04819
License (as archived): CC0-1.0
Archived: 2026-11-04T12:28:36.881Z
SHA-256 of the archived text: 4bd03a691e6b5f2a1163323cbca96b356893ab6e38a34f72d8070690e0ac3ce3
Stable URL of this page: https://metraipolis.com/documento/dc_ad5cb4e13151

How to cite

Reconstructing Fraudulent Strategies in Online Payments via Interpretable Inverse Reinforcement Learning — arXiv:2611.04819 — arXiv (en), https://arxiv.org/abs/2611.04819 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_ad5cb4e13151 (Metraipolis archive dc_ad5cb4e13151, sha256 4bd03a691e6b5f2a1163323cbca96b356893ab6e38a34f72d8070690e0ac3ce3)

BibTeX

@misc{dcad5cb4e13151,
  title = {Reconstructing Fraudulent Strategies in Online Payments via Interpretable Inverse Reinforcement Learning — arXiv:2611.04819},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.04819},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_ad5cb4e13151; sha256 4bd03a691e6b5f2a1163323cbca96b356893ab6e38a34f72d8070690e0ac3ce3},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.04819. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Payment fraud causes financial losses and undermines users' trust in web payment services. Although fraud detection is a critical defense for payment security, complex and diverse fraudulent behaviors can still evade detection. Generating fraud samples for data augmentation can broaden detectors' coverage of different fraud patterns. However, existing methods matching the distribution of transaction attributes alone do not ensure that generated sequences preserve the sequential relationships among transaction choices, payment feedback, and subsequent behavioral adjustments. To this end, we propose PaySeqGen, an interpretable inverse reinforcement learning framework that reconstructs fraud behavior policies from fraudsters' observed payment trajectories. We design a mechanism for constructing candidate actions to support plausible payment sequence generation while enabling exploration. The framework also learns interpretable behavioral rewards that capture fraudsters' preferences across different transaction contexts. By alternating between reward learning and policy optimization, the framework reconstructs policies that generate new fraud sequences for detector augmentation and learns interpretable rewards supporting sequential fraud-pattern analysis in real trajectories. Experiments in real-world payment scenarios at Ant International, a leading global digital payment provider, show that PaySeqGen outperforms state-of-the-art generative augmentation methods. Reward-based trajectory analysis also identifies fraud patterns of practical value, helping analyst locating suspicious segments and examine multistep fraud tactics. Evaluation in the deployed system of online payment further confirms its effectiveness.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2611.04819 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-04T12:26:47.177Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).