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

arXiv 2512.01439 ↗First posted 1 Dec 2025cs.CLcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 6 of its 19 references that are in the map. First posted on arXiv 1 Dec 2025.

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References · 6

  1. 2024The Llama 3 Herd of Modelscited by 14,192
  2. 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,907
  3. 2023G-Eval: NLG Evaluation using GPT-4 with Better Human Alignmentcited by 2,205
  4. 2021MuRIL: Multilingual Representations for Indian Languagescited by 177
  5. 2024Aya Expanse: Combining Research Breakthroughs for a New Multilingual Frontiercited by 121
  6. 2020Indic-Transformers: An Analysis of Transformer Language Models for Indian Languagescited by 22

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