arXiv AI November 2, 2026 By Ze Zhao, Haoyu Jiang, Weiyu Han, Ruobing Zuo, Xiao Tan, Shuai Chen, Tianyi Zhang

ID2Pattern: Pretraining Generalizable Identity Behavior Patterns for Transaction Risk Foundation Models

The Flow has not summarised this story yet — read it at arXiv AI.

Hugging Face Trending Papers

Aug 4

SeqLLM: Augmenting LLMs with Behavioral-Sequence Modeling for High-Stakes Decisions at WeChat Pay

Merchant risk control at large payment platforms screens tens of millions of merchants daily, where false positives harm legitimate merchants and false negatives leave harmful activity undetected. The hardest cases require jointly understanding a merchant's textual profile and long behavioral sequence.

llms rag fine-tuning benchmarks safety

arXiv AI

Jul 21

Detection, Attribution, Narration: An End-to-End Pipeline for Explainable Money Mule Identification

arXiv:2607. 17586v1 Announce Type: cross Abstract: Money mule accounts are critical facilitators of financial fraud, yet detecting them at scale remains challenging due to the heterogeneous nature of transactional and behavioural data.

By Yuge Zhang, Yuanxing Zhang, Yichao Jin, Khairul Amsyar Mohd Razis, Nicholas Qi An Choo, Kai Yin Anders Wong, Xinyan Tang, Kenneth Zhu Ke, Wee Keong Dennis Lee, Jingyuan Zhao

llms

arXiv Computation and Language

Aug 25

PRAGMA: Revolut Foundation Model

arXiv:2604.08649v2 Announce Type: replace-cross Abstract: Modern financial systems generate vast quantities of transactional and event-level data that encode rich economic signals. This paper present...

By Maxim Ostroukhov, Ruslan Mikhailov, Vladimir Iashin, Artem Sokolov, Andrei Akshonov, Vitaly Protasov, Andrey Goncharov, Dmitrii Beloborodov, Vince Mullin, Roman Yokunda Enzmann, Georgios Kolovos, Jason Renders, Pavel Nesterov, Anton Repushko

llms rag fine-tuning

arXiv Machine Learning

Jun 25

Multi-Stream Temporal Fusion for Financial Fraud Detection

arXiv:2606. 25007v1 Announce Type: new Abstract: Financial fraud detection in digital banking requires reasoning over multiple heterogeneous event streams -- transactions, login sessions, risk signals -- that individually appear benign but collectively reveal fraudulent patterns.

By Mohammadamin Dashti Moghaddam, Nick Sciarrilli

llms benchmarks safety

arXiv Machine Learning

2d ago

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

PersonaAgent is a retrieval‑augmented transaction anomaly detection framework that stores the latest entity behavior signals as refreshable personas in memory. It updates these personas weekly via a batch job and, during inference, retrieves them with a lightweight adapter to augment a base anomaly classifier’s scoring. Benchmarks at Visa show that PersonaAgent can process 1,000 transactions in 1–2 ms while improving PR‑AUC by 0.65–6.58 % and production business metrics by 8.93–14.24 % relative to baseline models.

By Zhengyu Fang, Liang Wang, Yingtong Dou, Yuzhong Chen, Shubham Jain, Junpeng Wang, Xiran Fan, Menghai Pan, Jiarui Sun, Yan Zheng, Yiwei Cai

rag agents benchmarks

arXiv AI

Oct 21

TxnQA: Benchmarking Analytical Reasoning and Evidence Grounding over Transaction Event Sequences

TxnQA is a new analytical QA benchmark that tests reasoning and evidence grounding over transaction event sequences. It contains 27 task families with 81 templates and a 5‑scenario fraud‑risk reasoning suite built on synthetic, privacy‑mitigated transaction data. The benchmark evaluates models using format‑following rate, conditional content accuracy, and end‑to‑end correctness, revealing distinct bottlenecks in schema compliance, semantic calculation, and evidence attribution.

By Rabimba Karanjai, Yiming Bian, Yun-Shiuan Chuang, Jenna L. Ballard, Zijie Pan, Shivani Shekhar, Yixi Li, Yirou Ge, Avinash Thangali, Richard Williamson, Linsey Pang, Prakhar Mehrotra

fine-tuning benchmarks safety