FinGround: Detecting and Grounding Financial Hallucinations via Atomic Claim Verification
Financial AI systems must produce answers grounded in specific regulatory filings, yet current LLMs fabricate metrics, invent citations, and miscalculate derived quantities. These errors carry direct regulatory consequences as the EU AI Act's high-risk enforcement deadline approaches (August 2026). Existing hallucination detectors treat all claims uniformly, missing 43% of computational errors that require arithmetic re-verification against structured tables. We present FinGround, a three-stage verify-then-ground pipeline for financial document QA. Stage 1 performs finance-aware hybrid retrieval over text and tables. Stage 2 decomposes answers into atomic claims classified by a six-type f...
| Authors | Dongxin Guo, Jikun Wu, Siu Ming Yiu |
|---|---|
| Primary category | cs.AI |
| Published | 26 April 2026 |
| Last updated at source | 26 April 2026 |
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