Conversational Finance: LLM-Powered Payment Assistant Architecture (Published)
This article explores the application of Large Language Models (LLMs) for conversational payment initiation and management within financial services. It proposes an intelligent assistant capable of securely handling financial transactions through natural language interfaces. The article addresses architectural components, natural language understanding, integration with payment systems, security protocols, and user authentication methodologies. The article examines implementation considerations including fraud detection, regulatory compliance, multi-modal interfaces, contextual awareness, and error handling. Through article evaluation of operational metrics and user experience data, the article demonstrates significant advantages of conversational payment systems over traditional interfaces. Despite notable limitations in privacy, cross-lingual capabilities, and integration with legacy systems, the article concludes that LLM-powered payment assistants represent a fundamental advancement in financial interaction, with promising directions for future research to enhance their sophistication, trustworthiness, and integration within the broader financial ecosystem.
Keywords: Financial Inclusion, conversational finance, large language models, natural language understanding, payment systems