Financial statements are critical for corporate analysis, fraud detection, and regulatory compliance, yet their complex tabular layouts, multi-year data structures, and hierarchical relationships pose significant challenges for automated reasoning. Existing Visual Question Answering (VQA) models struggle to deliver audit-ready responses due to their inability to explicitly localize evidence and reason over structured financial data. To bridge this gap, we introduce EviFiVQA, a large-scale benchmark dataset for evidence-based financial VQA, built on real-world financial statements. Leveraging FinTabNet, our dataset contains over 1.5 million question-answer pairs spanning five reasoning categories, each meticulously annotated with bounding boxes for relevant table cells to ensure traceable and interpretable responses. EviFiVQA introduces new challenges for Large Language Models (LLMs) and Vision-Language Models (VLMs) by requiring multi-step numerical reasoning, explicit evidence localization, and hierarchical financial aggregation—all within complex, irregular table layouts. We benchmark state-of-the-art models on EviFiVQA, revealing their limitations in evidence-grounded understanding of financial statements. We release EviFiVQA to drive progress in audit-ready financial AI, enhancing transparency and reliability in AI-driven financial analytics. The dataset and code are available at https://github.com/sachinraja13/EviFiVQA