arXiv:2602. 07294v4 Announce Type: replace-cross Abstract: With the increasing deployment of Large Language Models (LLMs) in the finance domain, LLMs are increasingly expected to parse complex regulatory disclosures.
By Yidong Jiang, Junrong Chen, Eftychia Makri, Jialin Chen, Peiwen Li, Ali Maatouk, Leandros Tassiulas, Eliot Brenner, Bing Xiang, Rex Ying
arXiv:2607. 01257v1 Announce Type: cross Abstract: The rapid digitalisation of financial systems has improved operational efficiency and financial inclusion while simultaneously increasing exposure to sophisticated forms of cyber-enabled fraud and electronic financial misconduct.
By Timothy Oluwapelumi Adeyemi, Abigail Omotola Ojogbede
arXiv:2608. 07400v1 Announce Type: new Abstract: Financial question answering is typically evaluated by answer correctness, yet in SEC filings a plausible and even numerically correct answer can be grounded in the wrong evidence.
By Sasan Mansouri, Daniel Saad, Mark Wahrenburg, Manu Weissel, Fabian Woebbeking
arXiv:2607. 19259v1 Announce Type: cross Abstract: Financial statement fraud detection (FSFD) is crucial for market integrity but faces challenges from increasingly sophisticated schemes and under-utilized textual data in financial reports.
By Guy Stephane Waffo Dzuyo (Forvis Mazars, LORIA CNRS Universit\'e de Lorraine), Ga\"el Guibon (LORIA CNRS Universit\'e de Lorraine, LIPN CNRS Universit\'e Sorbonne Paris Nord), Christophe Cerisara (LORIA CNRS Universit\'e de Lorraine), Luis Belmar-Letelier (Forvis Mazars)
arXiv:2606. 03031v1 Announce Type: new Abstract: Structured financial audit verification is difficult for language-model agents because correctness depends on structured evidence rather than text alone.
By Yan Wang, Xuguang Ai, Jaisal Patel, Xueqing Peng, Fengran Mo, Yupeng Cao, Haohang Li, Mingyu Cao, Lingfei Qian, V\'ictor Guti\'errez-Basulto
arXiv:2606. 18192v1 Announce Type: new Abstract: As high-quality public web corpora become increasingly exhausted, clean long-context documents have become a scarce and expensive source of training data for large language models (LLMs).
By Nick Bettencourt, Xiaowei Ding, Kay Giesecke