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This study introduces AWARE-FX, an auditable AI/NLP system designed to convert corporate annual report text into quantifiable measures of hedging disclosure related to foreign-exchange risk management. By leveraging a combination of professional-source lexicons, financial encoders, and an audit ledger, AWARE-FX processes over half a million snippets from nearly 25,000 firm-years, achieving high reliability in its assessments. Key findings reveal that while FinBERT outperforms other models in most tasks, the strict FX score is significantly associated with actual FX exposure, highlighting the system's potential for external validation of hedging practices.
AWARE-FX reveals that a specialized AI system can effectively quantify corporate hedging disclosures, providing insights that challenge the efficacy of generic LLMs in domain-specific contexts.
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.
