Large language models are reshaping finance, but production use demands far more than prompt engineering. Financial AI must reason over numbers, work with time-sensitive data, avoid leakage, support auditability, and operate within strict regulatory and model-risk controls.




LLMs in Finance provides an end-to-end guide to designing, evaluating, governing, and deploying language-model systems for financial workflows. You will learn the foundations of transformers, embeddings, attention, prompting, retrieval-augmented generation, and fine-tuning, then apply them to investment research, trading support, banking operations, fraud detection, credit, KYC, AML, compliance, and document intelligence.




The book also shows how to design financial agents that use tools, memory, retrieval, orchestration, and human oversight to complete complex tasks safely. Coverage of time-series applications, backtesting contamination, hallucination control, temporal validation, model risk, monitoring, and regulatory expectations helps you avoid the mistakes that make financial AI unreliable.




Practical Python examples, case studies, and a companion GitHub repository help you move from theory to implementation. By the end, you will be able to build scalable, auditable, production-ready LLM systems aligned with real business and regulatory constraints.