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PersonaAgent: Retrieval-Augmented Transaction Anomaly Scoring with Refreshable Multi-Entity Persona Memory — arXiv:2611.08094

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.08094
License (as archived): CC0-1.0
Archived: 2026-11-06T14:50:52.274Z
SHA-256 of the archived text: 27f3967691f1d7076a1caa1765b14d7f78acb0d547fa58dbf5aaf46a545ff342
Stable URL of this page: https://metraipolis.com/documento/dc_b77aba368eeb

How to cite

PersonaAgent: Retrieval-Augmented Transaction Anomaly Scoring with Refreshable Multi-Entity Persona Memory — arXiv:2611.08094 — arXiv (en), https://arxiv.org/abs/2611.08094 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_b77aba368eeb (Metraipolis archive dc_b77aba368eeb, sha256 27f3967691f1d7076a1caa1765b14d7f78acb0d547fa58dbf5aaf46a545ff342)

BibTeX

@misc{dcb77aba368eeb,
  title = {PersonaAgent: Retrieval-Augmented Transaction Anomaly Scoring with Refreshable Multi-Entity Persona Memory — arXiv:2611.08094},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.08094},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_b77aba368eeb; sha256 27f3967691f1d7076a1caa1765b14d7f78acb0d547fa58dbf5aaf46a545ff342},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.08094. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

In large-scale payment networks, entities (card, merchant, etc.) and their interactions play a key role in transaction anomaly detection. However, transaction patterns between different entities usually change more frequently than the production model refresh schedule. Meanwhile, entity embedding tables suffer from high-cardinality and sparse interaction issues. To resolve the above challenges, we present PersonaAgent, an agentic transaction anomaly detection framework which represents the latest entity behavior signals as personas and saves them in a refreshable memory. Offline, a weekly scheduled batch job is used to extract the latest behavior signals and update them in the memory. At inference time, a lightweight adapter retrieves the corresponding entity behavior signals of each transaction from memory and uses them to augment the base anomaly classifier's scoring. Through comprehensive benchmarking with different anomaly detection models at Visa, PersonaAgent is able to score transactions efficiently (a 1,000-transaction CPU batch in 1-2 ms), lifting the PR-AUC by 0.65-6.58% and the production business metric by 8.93-14.24% relatively in deployment.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.LG, nuovi lavori del giorno).
Categorie arXiv: Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2611.08094 — 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 2026-11-06T14:21:42.642Z dalla pagina https://arxiv.org/list/cs.LG/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).