AWARE-FX: An Auditable Knowledge-Guided AI System for Measuring Corporate Foreign-Exchange Hedging Disclosure
- URL: http://arxiv.org/abs/2607.27611v1
- Date: Thu, 30 Jul 2026 02:58:46 GMT
- Title: AWARE-FX: An Auditable Knowledge-Guided AI System for Measuring Corporate Foreign-Exchange Hedging Disclosure
- Abstract summary: Corporate annual reports contain weakly structured evidence about foreign-exchange risk management, derivative use, natural hedging, and explicit non-use.<n>This study develops AWARE-FX, an auditable AI/NLP decision-support system that converts report text into traceable firm-year hedging-disclosure measures.
- Score: 5.667104793245957
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Corporate annual reports contain weakly structured evidence about foreign-exchange risk management, derivative use, natural hedging, and explicit non-use. This study develops AWARE-FX, an auditable AI/NLP decision-support system that converts report text into traceable firm-year hedging-disclosure measures. The system combines a professional-source lexicon, negation and accounting-status logic, channel-specific financial encoders, exact evidence gates, conservative aggregation, and an audit ledger. Across 24,909 Hong Kong firm-years from 2008-2025, it retrieves and scores 543,527 snippets. Reliability is evaluated through ablations, a stratified 300-snippet human audit, three-seed FinBERT-ModernBERT comparisons, strict 2023-2025 temporal tests, probability calibration, selective prediction, and fixed-prompt generative-model benchmarks. FinBERT has the higher mean F1 in seven of eight encoder task-split comparisons; its temporal F1 ranges from 0.702 to 0.872. Abstaining on the 20% least-confident temporal observations raises retained-sample F1 by 0.050-0.077. Deterministic Qwen3-8B performs strongly on commodity and negation evidence but poorly on foreign-debt and accounting-context labels, showing that a general-purpose LLM does not uniformly replace domain constraints. The strict FX score is negatively associated with linked baseline and stress-period FX exposure, whereas the generic broad score is not. These associations provide external construct validation, not causal estimates of hedging effectiveness. AWARE-FX contributes a tested decision-support architecture in which retrieval, status logic, classification, uncertainty handling, aggregation, and external validation remain separately auditable.
Related papers
- Diagnosing Fine-Grained Inconsistency Classification in Financial Disclosure Text [3.2712318205295907]
Financial disclosures contain numerical claims, temporal statements, entity references, policy commitments, and risk descriptions that may conflict in qualitatively different ways.<n>We study this problem as fine-grained inconsistency.<n>A fine-tuned 300M encoder reaches 61.9% accuracy, compared with 61.5% for a LoRA-adapted Qwen3.5-9B model and 61.3% for GPT-adapted generative models.
arXiv Detail & Related papers (2026-07-29T01:03:53Z) - Explainable Artificial Intelligence for Anomaly Detection in Banking Transactions: An Internal Audit Perspective [0.0]
This paper introduces an Explainable Artificial Intelligence framework tailored for banking transaction anomaly detection within internal audit.<n>An Isolation Forest (iForest) model performs unsupervised anomaly scoring, while a SHAP layer provides transaction-level explanations grounded in cooperative game theory.<n>A lightweight Streamlit dashboard renders these outputs in a form to audit professionals without machine learning expertise.
arXiv Detail & Related papers (2026-07-15T05:58:41Z) - Multi-Agent Framework for Audit Risk Assessment with Explicit Uncertainty and Evidence Conflict Modeling [6.05989830654114]
UMAR is a framework that employs three specialized agents: an MD&A Text Agent, a Financial Ratio Agent, and a CAM Agent.<n>We evaluate UMAR on a U.S. dataset of 3,200 firm-year observations from SEC 10-K filings.
arXiv Detail & Related papers (2026-06-14T07:06:03Z) - Automated Benchmark Auditing for AI Agents and Large Language Models [46.03841647776303]
Auto Benchmark Audit (ABA) is an agentic framework that systematically audits individual benchmark tasks.<n>We run ABA on a collection of frontier LLM benchmarks and previous NeurIPS publications, totaling 168 benchmarks across nine domains.<n>ABA identifies critical issues including ambiguous task design, execution environment conflicts, and incorrect ground truths in over 25.7% of the evaluated tasks.
arXiv Detail & Related papers (2026-05-25T17:44:21Z) - TRUST: A Framework for Decentralized AI Service v.0.1 [47.384270414446604]
Large Reasoning Models (LRMs) and Multi-Agent Systems (MAS) in high-stakes domains demand reliable verification.<n>We introduce TRUST (Transparent, Robust, and Unified Services for Trustworthy AI), a decentralized framework with three innovations.<n>We prove a Safety-Profitability Theorem ensuring honest auditors profit while malicious actors incur losses.
arXiv Detail & Related papers (2026-04-29T19:32:58Z) - DeepFact: Co-Evolving Benchmarks and Agents for Deep Research Factuality [49.62610727661819]
Existing fact-checkers are primarily designed for general-domain, factoid-style atomic claims.<n>We propose Audit-then-Score (AtS), where benchmark labels and rationales are explicitly revisable.<n>We instantiate AtS as DeepFact-Bench, a versioned DRR factuality benchmark with auditable rationales.
arXiv Detail & Related papers (2026-03-06T05:05:57Z) - SSR: Safeguarding Staking Rewards by Defining and Detecting Logical Defects in DeFi Staking [55.62033436283969]
Decentralized Finance (DeFi) staking is one of the most prominent applications within the DeFi ecosystem.<n> logical defects in DeFi staking could enable attackers to claim unwarranted rewards.<n>We developed SSR (Safeguarding Staking Reward), a static analysis tool designed to detect logical defects in DeFi staking contracts.
arXiv Detail & Related papers (2026-01-09T15:01:41Z) - All That Glisters Is Not Gold: A Benchmark for Reference-Free Counterfactual Financial Misinformation Detection [67.89888669159899]
RFC Bench is a benchmark for evaluating large language models on financial misinformation under realistic news.<n>The benchmark defines two complementary tasks: reference free misinformation detection and comparison based diagnosis.
arXiv Detail & Related papers (2026-01-07T18:18:28Z) - Inferring Latent Market Forces: Evaluating LLM Detection of Gamma Exposure Patterns via Obfuscation Testing [0.0937899315060426]
Tests three dealer hedging constraint patterns on 242 trading days (95.6% coverage) of S&P 500 options data.<n>We find LLMs achieve 71.5% detection rate using unbiased prompts that provide only raw gamma exposure values.
arXiv Detail & Related papers (2025-12-08T15:48:57Z) - TrustJudge: Inconsistencies of LLM-as-a-Judge and How to Alleviate Them [58.04324690859212]
Large Language Models (LLMs) as automated evaluators (LLM-as-a-judge) has revealed critical inconsistencies in current evaluation frameworks.<n>We identify two fundamental types of inconsistencies: Score-Comparison Inconsistency and Pairwise Transitivity Inconsistency.<n>We propose TrustJudge, a probabilistic framework that addresses these limitations through two key innovations.
arXiv Detail & Related papers (2025-09-25T13:04:29Z) - FinAI-BERT: A Transformer-Based Model for Sentence-Level Detection of AI Disclosures in Financial Reports [6.324803752309524]
This study introduces FinAI-BERT, a domain-adapted transformer-based language model designed to classify AI-related content at the sentence level within financial texts.<n>The model was fine-tuned on a manually curated and balanced dataset of 1,586 sentences drawn from 669 annual reports of U.S. banks.
arXiv Detail & Related papers (2025-06-29T09:33:29Z) - Are You Getting What You Pay For? Auditing Model Substitution in LLM APIs [71.7892165868749]
Commercial Large Language Model (LLM) APIs create a fundamental trust problem.<n>Users pay for specific models but have no guarantee that providers deliver them faithfully.<n>We formalize this model substitution problem and evaluate detection methods under realistic adversarial conditions.<n>We propose and evaluate the use of Trusted Execution Environments (TEEs) as one practical and robust solution.
arXiv Detail & Related papers (2025-04-07T03:57:41Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.