Large Language Models in Finance

Build production-grade Large Language Model systems for finance. Learn how to design, fine-tune, evaluate, govern, and deploy LLMs, Retrieval-Augmented Generation (RAG), and AI agents for trading, banking, risk management, compliance, and financial research using rigorous mathematics, practical code, and real-world case studies.Key FeaturesBuild production-ready financial LLM systems with RAG, fine-tuning, AI agents, and MCPApply LLMs to trading, investment research, banking, risk, fraud, compliance, and documentsExplore reasoning models, multimodal AI, time-series LLMs, and autonomous financial agentsPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionLarge 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.What you will learnUnderstand LLM foundations for financial applicationsBuild financial LLM systems from ingestion to deploymentFine-tune models with LoRA, QLoRA, RLHF, and DPOCreate RAG pipelines for financial documents and knowledgeDesign autonomous agents and multi-agent finance workflowsIntegrate LLMs securely with MCP and enterprise systemsApply LLMs to trading, banking, risk, fraud, KYC, and AMLEvaluate and govern auditable financial AI with rigorous metricsWho this book is forThis book is written for data scientists, quantitative analysts, portfolio managers, traders, fintech developers, AI engineers, software architects, banking professionals, compliance specialists, regulators, researchers, and graduate students who want to apply Large Language Models to finance. Readers should have a fundamental understanding of Python programming, machine learning, and financial markets. The book is equally suitable for practitioners building production AI systems and researchers interested in the mathematical foundations of financial LLMs.