Financial Reporting and XBRL

Derniers papiers

• FinAutoRubric: Expert-Guided Automatic Rubric Generation for Evaluating Financial Research Agents

  Hoyoung Lee, Suyeol Yun, Jack Haverty, Yunju Cho, Meesong Kim, Daekyung Park, Sumin Kim, Jihoon Kwon, Jasmine Jia Geng, Andrew Chin, Yin Luo, Edward Tong, Yu Yu, Zach Golkhou, Minkyu Kim, Igor Halperin, Young Cha, Alejandro Lopez-Lira, Chanyeol Choi, Yongjae Lee · 29 septembre 2026

  Evaluating finance research agents requires rubrics that reflect expert standards and fix the values correct as of an information cutoff. Expert-reviewed finance benchmarks rely on fixed, per-item rubrics, which are costly to extend and cannot encode each institution's own standard. In FinAutoRubric…

• Behavior-Grounded Semantic Enrichment for Financial Fraud Modeling and Reasoning

  Linbo Shao, Huilin He, Yating Lou, Dawei Cheng · 29 septembre 2026

  In financial fraud detection, rich semantic context can provide important evidence for transaction behavior modeling and fraud reasoning. However, public real-world financial datasets often lack rich semantics due to privacy constraints. Consequently, synthetic datasets incorporate generated semanti…

• BudgetVerify: Budget-Tiered Verification for Financial QA

  Janet Jenq, Hongda Shen · 29 septembre 2026

  Financial question answering often requires precise numerical extraction, unit handling, and arithmetic over tables and text, but applying expensive verification uniformly wastes test-time compute. We propose BudgetVerify, a budget-tiered generator-verifier framework that routes each generated answe…

• FinancialAuditBench: Benchmark Construction under Differential Privacy Using Real-World Priors

  Jerry Huang, Sarvesh Babu, Matt Van Buren, Alexander Wang, Pranav Pillai, Arush Jain, James P. Burton, Julia Hockenmaier · 29 septembre 2026

  As AI agents are becoming widely adopted in the financial services industry, careful measurement is essential to understand where they can be reliably deployed and where oversight and professional review remain necessary. Such measurement, however, is constrained by limited access to proprietary or …

• FinFIRST: Benchmarking Search Agents for Financial Information Retrieval, Sourcing and Traceability

  Wenqing Wang, Haitao Xiang, Xinyi Zhao, Mingming Yin, Ying Zhong, Zhaoxin Huan, Qiheng Zhou, Jin Zhu, Xiaolu Zhang, Shi Chang, Jun Zhou · 23 septembre 2026

  Financial search is a highly demanding task for LLM agents, requiring not only a correct final answer but also temporally valid information retrieval, authoritative source selection, entity and period alignment, unit and definition consistency, and verifiable evidence for all conclusions. Existing b…

• Data-Centric Post-Training for Financial Reasoning: Mining, Distillation, and Verifiable Learning

  Zhirayr Hayrapetyan, Andrei Kalmykov, Denis Kokosinskii, Dmitry Stanishevskii, Dmitry Zmitrovich · 10 septembre 2026

  Financial text, textbooks, and question-answer pairs are abundant, but only a small fraction is directly usable for reasoning-focused post-training. Existing QA pairs often lack explicit reasoning, sufficient context, or reliably verifiable answers, while textbooks must first be transformed into syn…

• IGT @ FinMMEval 2026 Task 2: Question-Type Prompting with Targeted Extraction for Multilingual Financial QA

  Yuwen Chiu (Georgia Institute of Technology) · 9 septembre 2026

  We present the IGT system for PolyFiQA Task 2 of the FinMMEval Lab at CLEF 2026, a multilingual financial question answering task over English SEC filings and multilingual news articles (English, Chinese, Japanese, Spanish, Greek) for four companies. Our central observation is that the 344 developme…

• When Financial Fine-tuning Fails: A Three-Level Detectability Analysis of Numerical Hallucination in Domain-Adapted Language Models

  Xiaodong Li, Peiwei Liu · 7 septembre 2026

  Financial large language models are increasingly deployed for summarization of reports and disclosures, where numerical hallucination poses significant practical risks. While prior work often attributes such hallucination to insufficient numerical reasoning, this assumption has not been systematical…

• Enhancing Financial Question Answering: A Novel Benchmark Dataset of Banks' financial statements

  Arianna Miola, Bruno Spaccavento, Lorenzo Silotto, Marco Bianchetti, Luca Cagliero · 4 septembre 2026

  The comparative analysis of banks' financial statements poses significant challenges for automated question answering systems due to their complexity, substantial length, technical language, and inhomogeneity of both textual and numerical content across different jurisdictions and institutions. We i…

• FinLifeBench: Exhaustive Life-Event History and Financial-State Reconstruction from Longitudinal Banking Dialogue

  Hangyeul Lee, Juyoung Oh, Jaeyong Ko, Sunmin Kim, Jaeik Park, Hyunkyu Kim, Jungmin Son, Pilsung Kang · 2 septembre 2026

  Repeated banking interactions require assistants to maintain complete, current, and traceable customer records as life changes emerge incidentally in routine requests. Existing benchmarks emphasize question answering, bounded episodes, or targeted recall rather than exhaustive longitudinal reconstru…

• AnalysisBank: An Expert Analysis Pattern Library for Financial Report Generation

  Yajing Yang, Yunshan Ma, Kelvin J. L. Koa, Min-Yen Kan · 2 septembre 2026

  We argue that financial report generation should operate at the analytical rather than structural level, composing content from data-derived insights rather than high-level topics or sections. To this end, we propose AnalysisBank, which distills expert reports into a reusable library of Analyses, ea…

• FinExam-10K: When Retrieval Helps Financial Reasoning?

  Yan Lin, Jingyu Sun, Zhongliang Guo, Qing Li, Zhuohan Xie, Yuxia Wang · 31 août 2026

  Professional financial examinations require models to combine domain knowledge, calculation, and judgment, yet no benchmark covers the full CFA and FRM structure under one protocol. We introduce FinExam-10K, to our knowledge the largest reported English benchmark for this setting, with 10,198 expert…

• VERA-8B: Evidence-Grounded Audit Risk Reasoning from SEC Filings

  Menghan Liu, Elynn Chen · 31 août 2026

  Across audit applications, judgments must be supported by reasonable evidence. However, standard financial language models prioritize fluency over evidence. They are built for general financial reasoning and may produce plausible but ambiguous answers, creating a grounding gap that makes them unsuit…

• From Documents to Reasoning: A Validated Synthetic Data Pipeline and Semantic-Aware Fine-Tuning for Financial Numerical Reasoning

  Lokendra Birla, Milind Savagaonkar, Visnu Srinivasan, Sowmya Rasipuram, Shubhashis Sengupta · 31 août 2026

  Financial question answering (QA) has emerged as a key benchmark for evaluating the performance of Large Language Models (LLMs) on domain-specific tasks involving complex data formats such as tables, charts, and rich textual narratives. While recent advancements have enabled models to reason across …

• Towards Expert Financial QA via Self-Improving RAG

  Junjie Xiong, Shawheen Ghezavat, Aum Hirpara · 28 août 2026

  Expert-level financial question answering requires both grounded verification to catch numeric hallucinations and audit trails for regulatory compliance, attributes that standard single-pass RAG systems lack. We take a step toward this goal with Self-Improving RAG, a framework that decomposes docume…

• CIFQA: A Deterministic Tool-Grounded Multi-Agent LLM Framework for Financial Query Answering

  Kunjesh Parekh, Anil Kumar Tiwari, Divya Saxena · 28 août 2026

  Calculation-intensive financial question answering requires exact reasoning over structured rates, temporal conditions, numerical formulas, and rule-based constraints. Although Large Language Models (LLMs) perform strongly on natural language tasks, they often produce numerically incorrect yet plaus…

• FinRiskAtlas: Decision-Aligned Evaluation of Large Language Models for Financial Risk Review

  Suyang Zhong, Jingzhe Zhu, Qi Xu, Liyao Sun, Yin Wang, Qingqing Sun, Shuai Chen, Tianyi Zhang · 27 août 2026

  Deploying large language models for professional financial review requires more than measuring general financial competence: models must perform the specific review operation required by a workflow and determine whether available evidence is sufficient for a defensible decision. Existing financial b…

• FinixDoc: Rethinking Financial Document Parsing Beyond Saturated Benchmarks

  Hang Wang, Jin Zhang, Guoliang Xu, Pengyue Lu, Yao Li, Zijiao Zhang, Tianyu Huang, Weiqi Xiong, Yulong Wang, Chuqiao Lu, Wenkang Huang, Kai Yang, Yadong Li, Hui Li, Xingzhong Xu, Xiao Xu · 25 août 2026

  Financial document parsing requires accuracy, structural consistency, and verifiability that current benchmarks often fail to reflect. We present FinixDoc, an end-to-end agentic parsing system for real-world financial documents, with FinixDoc-VL, a 4B-scale vision-language model built on Qwen3-VL-4B…

• Reliable Financial Named Entity Recognition under Domain Shift

  Zihao Zheng, Baichuan Li, Junyi Yao, Jiayu Long · 21 août 2026

  Financial AI systems often train information extractors on one textual register and deploy them across filings, news, and user-generated content, while standard F1 scores do not indicate which predictions remain safe to automate when the input distribution changes. We study confidence estimation and…

• FinRCA-Bench: Benchmarking Evidence Retrieval and Reasoning for Financial AI Systems

  Pratik Ghawate · 20 août 2026

  Large language models are increasingly used to support financial operations, but their apparent reasoning performance can depend on whether they receive the right evidence. In financial reconciliation, the evidence needed for diagnosis is distributed across invoices, purchase orders, approvals, allo…

• LAVA: Logic-Aware Validation and Augmentation Framework for Large-Scale Financial Document Auditing

  Ruoqi Shu, Xuhui Wang, Isaac Wang, Yanming Mai, Bo Wan · 18 août 2026

  Financial document validation in production, such as payroll auditing, tax compliance, and loan underwriting, demands exceptional accuracy, consistency, and reproducibility under strict enterprise constraints. In practice, documents arrive with heterogeneous layouts and formats, semantically rich an…

• Are Large Language Models Reliable Reviewers? A Benchmark for Error Detection in Financial Documents

  Ying He, Zhouhong Gu, Zhecheng Hu, Yubo Zhou, Hao Shen, Jiaqing Liang, Zhaoqian Dai, Shuguang Ma, Fei Yu, Yanghua Xiao, Zhixu Li · 14 août 2026

  Ensuring the accuracy of financial documents is critical for economic analysis, regulatory compliance, and corporate decision-making. Several studies have shown that Large Language Models (LLMs) perform well in many financial tasks, such as stock price movements and financial analytics. However, a c…

• V-FiLLM: Verified Financial LLM Reasoning Benchmark

  Alicia Larsen, Victoire Laurent, Aulia Kharis Rakhamsari, Lara Turgut, Nino Antulov-Fantulin · 12 août 2026

  While existing benchmarks have made substantial progress in evaluating LLMs across STEM domains, financial reasoning over structured data remains comparatively less explored. We introduce V-FiLLM, a framework that generates financial reasoning benchmarks from executable computation trees grounded in…

• Can Open-Weight Models Compete on Financial Text Comprehension?

  Jan Sp\"orer · 11 août 2026

  Open-weight language models from Chinese AI labs caught up on benchmarks relative to proprietary frontier models in recent months. Yet their reliability on real-world financial tasks remains largely untested. We updated the Financial Touchstone benchmark, which now has 2,967 question context-answer …

• FinRank: An Evidence-Grounded Benchmark for Financial Question Answering and Retrieval over SEC Filings

  Sasan Mansouri, Daniel Saad, Mark Wahrenburg, Manu Weissel, Fabian Woebbeking · 10 août 2026

  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. Similar facts and disclosures recur across sections of a filing, across reporting periods of the same firm, and across …