EvidenceLens: A Claim-Evidence Matrix for Auditing Financial Question Answering
Authors
Fengchen Gu (Xi’an Jiaotong-Liverpool University), Xiaotian Ren (Xi’an Jiaotong-Liverpool University), Zhengyong Jiang (Xi’an Jiaotong-Liverpool University), Zhilu Zhang (Xi’an Jiaotong-Liverpool University), Ángel F. García-Fernández (Universidad Politécnica de Madrid), Angelos Stefanidis (Xi’an Jiaotong-Liverpool University), Mian Zhou (Xi’an Jiaotong-Liverpool University), Huakang Li (Xi’an Jiaotong-Liverpool University), Jionglong Su (Xi’an Jiaotong-Liverpool University)
Presentation
- Session
- Can We Trust This Chart? (Asking for a Friend)
- Time
- Wednesday, Nov 11, 09:03 – 09:12 (US/Eastern) · session 08:00 – 09:30
- Location
- Hall America south
Links
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Keywords
Financial question answering, provenance, visual analytics, multimodal evidence, trust in AI
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 representation is a multimodal claim-evidence matrix that exposes coverage, contradiction, and modality imbalance. To support reproducibility, we specify a JSON-based artifact schema, an inspectable multimodal alignment pipeline, and a deterministic review-priority ranking that maps backend signals into an auditable visual structure. Through two report-auditing scenarios, we illustrate how the interface makes grounded, weakly supported, and contradicted claims easier to inspect than in a linear chat transcript.
For Practitioners
Relevant practitioners include financial analysts, data scientists, fintech teams, and compliance/risk professionals. They can use this work to audit LLM-generated financial answers by checking each claim against text, tables, and charts.