Multilingual Conversational AI for Financial Assistance: Bridging Language Barriers in Indian FinTech

2025-12-01

AI Summary

Financial inclusion in India is hindered by extreme linguistic diversity—31 major languages coexist, yet only 10% of the population is proficient in English, creating significant barriers for low-resource language users in fintech interactions. Method: We propose a multilingual conversational AI system tailored for financial services, featuring a novel multi-agent architecture that jointly orchestrates language identification, functional routing, and response generation. The system natively supports code-mixed languages (e.g., Hinglish), integrates multilingual large language models, lightweight function routing, and domain-customized response generation, and is optimized on real-world financial dialogue data for semantic fidelity and inference efficiency. Contribution/Results: Deployment demonstrates substantial improvement in user engagement, with only a 4–8% increase in end-to-end latency. The system achieves high stability and computational efficiency across multilingual financial tasks, effectively overcoming longstanding resource scarcity constraints in low-resource language fintech applications.

Technology Category

• Natural Language Processing: Machine Translation, Multilinguality, Cross-Lingual NLP
• Machine Learning: Large Multimodal Models (LMMs)
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

India's linguistic diversity presents both opportunities and challenges for fintech platforms. While the country has 31 major languages and over 100 minor ones, only 10% of the population understands English, creating barriers to financial inclusion. We present a multilingual conversational AI system for a financial assistance use case that supports code-mixed languages like Hinglish, enabling natural interactions for India's diverse user base. Our system employs a multi-agent architecture with language classification, function management, and multilingual response generation. Through comparative analysis of multiple language models and real-world deployment, we demonstrate significant improvements in user engagement while maintaining low latency overhead (4-8%). This work contributes to bridging the language gap in digital financial services for emerging markets.

Problem

• Addresses language barriers in Indian FinTech for financial inclusion
• Develops multilingual conversational AI supporting code-mixed languages like Hinglish
• Improves user engagement with low latency in digital financial services

Innovation

• Multilingual conversational AI for financial assistance
• Supports code-mixed languages like Hinglish
• Multi-agent architecture with language classification and response generation