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

TL;DR

This paper introduces a multilingual conversational AI system tailored for India's diverse linguistic landscape, enhancing financial assistance accessibility through code-mixed language support and real-world deployment.

Contribution

It presents a novel multi-agent architecture supporting code-mixed languages like Hinglish, improving user engagement in financial services for multilingual populations.

Findings

Significant increase in user engagement metrics.

Low latency overhead of 4-8%.

Effective handling of code-mixed language interactions.

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.