A global LLM lab partnered with Turing to identify systematic failure points in financial reasoning. Using an extended FinQA-inspired framework, we built a human-annotated dataset to probe how models handle complex logic, multi-modal data, and grounded arithmetic, based on real annual reports and regulatory disclosures.

Frontier models continue to hallucinate, overfit, or misfire when asked to reason over financial documents that span 100–300 pages. The client needed a dataset that would:

• Surface realistic, model-breaking QA grounded in long-span annual reports, regulatory footnotes, and earnings disclosures
• Target known weaknesses like ratio chaining, unit normalization, multi-hop table traversal, and legal-text arithmetic
• Provide interpretable chain-of-thought (CoT) explanations to reduce hallucinations and help close the performance gap between leading models and expert-human accuracy on finance QA benchmarks like FinQA
• Simulate expert-level reasoning across heterogeneous sources, such as structured tables, unstructured text, footnotes, and embedded figures, where key values are often split across pages, disclosures, and document types
• Define a new standard of “hard” in financial QA, where even SOTA models fail, and only certified finance professionals can author the task

Dataset

• Our team created 10,000+ prompts grounded in annual financial reports, designed to expose and quantify failure points in SOTA models, including:
  a. 1 prompt + long-form CoT (10–25 steps) written in easy-to-understand, interpretable logic
  b. Annotation references/grounding to specific charts, tables, infographics and narrative text
  c. A structured difficulty mix with ≥55% hard examples per batch
• All prompts were human-generated  
• Industries: BFSI, Logistics, Tech, FMCG and more

Example: “Calculate the Net Debt to Total Capitalization Ratio for FY2018 using footnote-disclosed lease liabilities and split disclosures across pages 34 and 98.”

Talent and QA

We embedded high-signal human intelligence at every layer:

• 500+ vetted finance experts (CA/CFA/CPA)  
• Multi-layer QA:
  a. L1: Manual review by domain experts for accounting accuracy (e.g., IFRS/GAAP variance, footnote extraction)
  b. L2: Programmatic checks on CoT structure, logical flow, and multi-hop consistency
  c. L3: LLM-assisted expert review to ensure reasoning diversity, coverage, and final accuracy
• Average quality score: 4.9/5 across 10,000+ prompts

• Achieved 97% annotation accuracy across 10,000+ prompts, as measured through multi-tier QA including SME validation and LLM-assisted audit
• Estimated to improve accuracy by 4–6 percentage points on grounded financial QA tasks during fine-tuning or eval cycles
• 100% of prompts were structured to target known model weaknesses: multi-hop ratios, cross-table chaining, disclosure tracing, and conditional logic

With this new dataset, the client can:

• Improve reasoning quality on finance-specific LLM benchmarks
• Train reward models to penalize unsupported or speculative answers
• Stress-test internal models during eval cycles or leaderboard comparisons
• Build interpretable CoT traces for user-facing financial applications