Large language models (LLMs) increasingly assist financial analysis tasks over filings, reports, and news via retrieval-augmented generation (RAG), but a human reviewer typically remains accountable for the final decision. In this setting, a confidence score must be more than well-calibrated: it must be evidence-based and explainable, so reviewers can see why an answer should be trusted. We introduce CAST (Confidence via Answer Support Testing), a Bayesian-inspired confidence method that compares a generated answer with a plausible competing alternative and evaluates how retrieved passages support each hypothesis. CAST produces both an answer-level confidence score and citation-level evidence for review. We also demonstrate CAST through an interactive reviewer-facing tool that visualizes supporting and opposing evidence for human verification. On 1,286 financial-report questions from MMDocRAG, CAST achieves the highest AUROC among seven evaluated methods (0.634 vs. 0.598 for the strongest baseline).