FiMI: A Domain-Specific Language Model for Indian Finance Ecosystem

2026-02-05

AI Summary

This work addresses the limitations of existing large language models in the Indian financial ecosystem—particularly in domains such as digital payments, transaction disputes, and authorization management—and the absence of multilingual models supporting English, Hindi, and Hinglish. To bridge this gap, the authors present FiMI, the first large multilingual model tailored for Indian financial scenarios, built upon the Mistral Small 24B architecture. FiMI is developed through continued pretraining on 68 billion tokens of multilingual financial and synthetic data, followed by instruction tuning for multi-turn tool-augmented dialogues and domain-specific supervised fine-tuning. Experimental results demonstrate that FiMI Base outperforms baseline models by 20% on financial reasoning benchmarks, while FiMI Instruct achieves an 87% improvement in tool-calling tasks, all while maintaining competitive general-purpose capabilities.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Language Grounding & Multi-modal NLP
• Computer Vision: Large Vision Models

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

We present FiMI (Finance Model for India), a domain-specialized financial language model developed for Indian digital payment systems. We develop two model variants: FiMI Base and FiMI Instruct. FiMI adapts the Mistral Small 24B architecture through a multi-stage training pipeline, beginning with continuous pre-training on 68 Billion tokens of curated financial, multilingual (English, Hindi, Hinglish), and synthetic data. This is followed by instruction fine-tuning and domain-specific supervised fine-tuning focused on multi-turn, tool-driven conversations that model real-world workflows, such as transaction disputes and mandate lifecycle management. Evaluations reveal that FiMI Base achieves a 20% improvement over the Mistral Small 24B Base model on finance reasoning benchmark, while FiMI Instruct outperforms the Mistral Small 24B Instruct model by 87% on domain-specific tool-calling. Moreover, FiMI achieves these significant domain gains while maintaining comparable performance to models of similar size on general benchmarks.

Problem

• domain-specific language model
• Indian finance ecosystem
• digital payment systems
• tool-calling
• financial reasoning

Innovation

• domain-specific language model
• financial NLP
• instruction fine-tuning
• multilingual finance data
• tool-augmented reasoning