{"ID":2834403,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.01439","arxiv_id":"2512.01439","title":"Multilingual Conversational AI for Financial Assistance: Bridging Language Barriers in Indian FinTech","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.","short_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 a...","url_abs":"https://arxiv.org/abs/2512.01439","url_pdf":"https://arxiv.org/pdf/2512.01439v1","authors":"[\"Bharatdeep Hazarika\",\"Arya Suneesh\",\"Prasanna Devadiga\",\"Pawan Kumar Rajpoot\",\"Anshuman B Suresh\",\"Ahmed Ifthaquar Hussain\"]","published":"2025-12-01T09:23:13Z","proceeding":"cs.CL","tasks":"[\"cs.CL\"]","methods":"[\"Language Model\"]","has_code":false}