Large Language Models (LLMs) are being adopted in financial workflows, where accuracy and reliability are critical. One such application is financial question answering over public company filings. However, LLMs struggle with quantitative financial reasoning, including identifying financial facts, interpreting reporting periods and units, and determining appropriate metrics, leading to incorrect answers. Recent RAG-based methods have improved financial question answering but do not ensure factual and numerical correctness. LLM-based verifiers may also accept incorrect numerical claims. To address this, we introduce VeriFin, a neurosymbolic framework for verifying LLM generated numerical financial claims. Given a question and a generated answer, VeriFin retrieves filing evidence, grounds values in XBRL facts, and establishes the required formula independently of the answer. It then uses the Z3 SMT solver to check the resulting arithmetic constraints. VeriFin accepts consistent claims, rejects inconsistent claims, and abstains when required facts or formulas cannot be established. To evaluate VeriFin, we constructed XBRLFiling, a dataset of 600 numerical questions from corporate filings, and selected 67 and 403 questions from FinanceBench and TAT-DQA, respectively. Across six answer models and three datasets, VeriFin achieves 100% observed precision among accepted answers with known correctness. For Qwen3-30B on XBRLFiling, VeriFin accepts 304 correct answers compared to 166 for an LLM judge, while reducing false accepts from 87 to 0. We release our benchmark to support research on reliable financial question answering.

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