EvidenceLens: A Claim-Evidence Matrix for Auditing Financial Question Answering

2026-06-19

AI Summary

Large language models frequently generate assertions in financial question answering that blend well-supported claims, weakly reasoned statements, and unsupported conclusions, thereby compromising the reliability of high-stakes decisions. This work addresses the issue by framing it as an assertion–evidence alignment task and introduces a multimodal assertion–evidence matrix that systematically links textual, tabular, and graphical evidence through a lightweight alignment pipeline and standardized JSON evidence artifacts. Integrating a deterministic audit-prioritization algorithm with an interactive visualization interface, the proposed approach significantly enhances analysts’ ability to discern reliable assertions from overconfident or unsubstantiated content in financial statement audits, outperforming conventional chat-based information presentation methods.

Abstract

Large language models are increasingly used to answer questions over annual reports, earnings decks, and analyst notes, yet their outputs remain difficult to verify in high-stakes financial workflows. A fluent answer can blend directly grounded statements, weak synthesis, and unsupported claims across narrative text, tables, and charts. We present EvidenceLens, a visual analytics prototype that treats financial question answering as a claim-evidence alignment problem. The system decomposes an answer into atomic claims, summarizes support composition and confidence, support gaps, and coordinates claim-level inspection with source passages, table cells, and chart regions. Its core visual representation is a multimodal claim-evidence matrix that makes coverage, contradiction, and modality imbalance immediately visible. To support reproducibility, we also specify a JSON-based artifact schema, a lightweight multimodal alignment pipeline, and a deterministic review-priority ranking that maps backend signals into an auditable visual structure. Through representative report-auditing scenarios, we show how EvidenceLens helps analysts distinguish grounded claims from overconfident synthesis that conventional chat interfaces flatten.

Problem

• financial question answering
• claim-evidence alignment
• auditability
• multimodal verification
• large language models

Innovation

• claim-evidence alignment
• multimodal matrix
• financial question answering
• visual analytics
• auditable AI