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.
翻译：印度的语言多样性为金融科技平台带来了机遇与挑战。该国拥有31种主要语言和超过100种次要语言，但仅有10%的人口理解英语，这为金融普惠带来了障碍。我们提出了一种用于金融援助场景的多语言对话式人工智能系统，该系统支持诸如印地英语（Hinglish）等语码混合语言，能够为印度多样化的用户群体提供自然的交互体验。我们的系统采用多智能体架构，包含语言分类、功能管理和多语言响应生成模块。通过对多种语言模型的比较分析及实际部署，我们证明了该系统在保持低延迟开销（4-8%）的同时，显著提升了用户参与度。本工作有助于弥合新兴市场数字金融服务中的语言鸿沟。
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