FIND: Toward Multimodal Financial Reasoning and Question Answering for Indic Languages

TL;DR

This paper introduces FinVQA, a comprehensive benchmark for evaluating multilingual financial reasoning in Indic languages, and proposes FIND, a framework enhancing numerical and multimodal reasoning accuracy.

Contribution

The paper presents a new multilingual financial reasoning benchmark and a novel framework that improves numerical and multimodal reasoning in high-stakes financial contexts.

Findings

FinVQA contains 18,900 samples across six Indic languages and multiple financial domains.

FIND improves numerical reasoning accuracy through constraint-aware decoding.

The combined benchmark and framework set a new standard for multilingual financial reasoning evaluation.

Abstract

Financial decision-making in multilingual settings demands accurate numerical reasoning grounded in diverse modalities, yet existing benchmarks largely overlook this high-stakes, real-world challenge, especially for Indic languages. We introduce FinVQA, a benchmark for evaluating financial numerical and multimodal reasoning in multilingual Indic contexts. FinVQA spans English, Hindi, Bengali, Marathi, Gujarati, and Tamil, and comprises 18,900 samples across 14 financial domains. The dataset captures diverse reasoning paradigms under realistic constraints, and is structured across three difficulty levels (easy, moderate, hard) and four question formats: multiple choice, fill-in-the-blank, table matching, and true/false. To address these challenges, we propose FIND, a framework that combines supervised fine-tuning with constraint-aware decoding to promote faithful numerical reasoning, robust multimodal grounding, and structured decision-making. Together, FinVQA and FIND establish a rigorous evaluation and modeling paradigm for high-stakes multilingual multimodal financial reasoning.