arXiv:2609.34211v2 Announce Type: replace
Abstract: In financial fraud detection, rich semantic context can provide important evidence for transaction behavior modeling and fraud reasoning. However,...
By Linbo Shao, Huilin He, Yating Lou, Dawei Cheng
arXiv:2609.06063v1 Announce Type: new
Abstract: AI Agents are increasingly deployed in real-world settings, where they interact with external tools and make sequential decisions with limited human ov...
By Vittoria Vineis, Fabiano Veglianti, Lorenzo Antonelli, Claudia Di Carlo, Matteo Silvestri, Gabriele Tolomei
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
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
GRACE is a step‑level benchmark for evaluating the faithfulness of chain‑of‑thought reasoning over context. It provides human annotations for each step in CoT traces from 10 models across 4 datasets, labeling faithfulness, error category, and natural‑language explanations. The benchmark introduces a data‑driven taxonomy that splits errors into GRACE‑Inference (deductive) and GRACE‑Grounding (factual) tracks, each with four categories, and demonstrates that incorporating step‑level faithfulness signals can improve downstream accuracy and reasoning reliability.
By Hoang Pham, Dong Le, Anh Tuan Luu
The paper proposes a three‑engine architecture for reliable financial AI, combining a financial knowledge graph, a deterministic optimization engine, and a multi‑agent LLM layer. A coordination layer called context engineering selects and validates context from ERP systems, tools, and tenants to ensure numerical accuracy, traceability, and auditability. Observations from 15 deployments show that graph‑backed multi‑agent validation can reduce hallucination rates from 7‑9% to below 1%, while reusable mappings and validation policies cut deployment time from six to two weeks.