Large Language Models in FinTech: Applications in Financial Analysis, Customer Service, and Risk Management

Abstract

Large language models (LLMs) are changing how financial institutions and FinTech firms work with text, from earnings reports and regulatory filings to customer conversations and complaint records. Their promise is considerable: faster analysis, more responsive service, and earlier detection of emerging risks. Their risks are equally significant: fabricated facts, weak numerical reasoning, bias, data leakage, and actions no one can explain. This article examines LLM applications across three FinTech domains: financial analysis, customer service, and risk management. It proposes an architecture for deploying them responsibly. The architecture combines finance-adapted language models, retrieval-augmented generation (RAG) over institutional knowledge, guardrails and governance, scalable service delivery, configurable risk and decision workflows, and autonomous operations. Using a design-oriented approach grounded in ten studies published between 2019 and 2025, the article maps the evidence base, presents the architecture and the flow of a customer request, and classifies applications by LLM task, risk level, and required controls. It also sets out the main risks with their mitigations. It illustrates the architecture with a digital bank that uses LLMs in credit analysis, customer disputes, and complaint-based risk monitoring. It argues that LLMs create lasting value in FinTech when they are grounded in verified data, matched to the risk of each task, and embedded in governed workflows.