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

Hazarika, Bharatdeep, Suneesh, Arya, Devadiga, Prasanna, Rajpoot, Pawan Kumar, Suresh, Anshuman B, Hussain, Ahmed Ifthaquar

arXiv.org Artificial Intelligence 

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.

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