NTU and NUS Joint Research Identifies APAC-Specific LLM Failure Modes in Financial Document Processing

Research from Nanyang Technological University and the National University of Singapore has documented a taxonomy of LLM failure modes specific to APAC financial document processing — identifying systematic errors that general-purpose LLMs produce when applied to financial documents in APAC languages and formats without domain-specific fine-tuning or validation frameworks.

The research evaluated five leading LLMs (GPT-4o, Claude 3.5 Sonnet, Gemini 1.5 Pro, Qwen2.5-72B, and Llama 3.1 70B) on a benchmark of 2,400 financial documents across Singapore, Hong Kong, Japan, and Taiwan — including annual reports, prospectuses, loan documents, and regulatory filings in English, Mandarin, and Japanese. The taxonomy of identified failure modes includes currency confusion (SGD vs HKD vs AUD vs USD in multi-currency documents), date format misinterpretation (DD/MM/YYYY vs MM/DD/YYYY vs Japanese era calendar systems), Mandarin financial term ambiguity (terms with different regulatory meanings across mainland China, Hong Kong, and Taiwan), and structured table extraction errors from PDFs with complex APAC typographic conventions.

For APAC FSI institutions evaluating LLM deployment for document automation, the NTU/NUS research provides a validation framework — a benchmark test set that can be used to evaluate LLM performance on APAC financial document types before production deployment. The research recommends domain-specific evaluation (not just general benchmarks) and structured validation workflows before deploying LLMs in financial document automation contexts where error rates have direct compliance and financial consequences.