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Full Description
Implement Large Language Models to transform finance with this hands-on guide. Build AI agents, automate trading and banking, enhance risk management, and streamline compliance with real examples.
Key Features
Implement LLM solutions for trading, investment analysis, and banking operations
Build Financial agents to automate financial tasks in risk management and compliance
Explore real-world case studies, advanced model techniques, and future AI trends
Purchase of the print or Kindle book includes a free PDF eBook
Book DescriptionLarge language models are revolutionizing finance from market analysis to compliance automation but applying them in practice can be challenging. LLMs in Finance is a step-by-step guide that demystifies these powerful models and shows you how to implement them in real financial projects. You'll begin with the fundamentals of how LLMs work, including the transformer architecture, prompt engineering, and fine-tuning for financial data. Next, you'll build applications: intelligent agents for trading; tools for banking tasks like credit scoring and fraud detection; and automated solutions for risk management and regulatory compliance. To deploy these solutions, you'll learn how to scale LLM implementations, from choosing the right hardware to adopting effective cloud strategies. You'll also explore advanced methods to improve model performance, including reinforcement learning for fine-tuning. Finally, learn how to address AI bias and ensure your LLM solutions comply with ethical and regulatory requirements. Real-world case studies illustrate how financial organizations are leveraging LLMs in trading systems, customer service chatbots, and more.
By the end of the book, you'll have the expertise to build LLM-powered applications and pick up on trends such as multimodal AIWhat you will learn
Master cutting-edge LLM implementations for trading strategy development and investment analysis
Transform risk assessment frameworks using LLMs for credit scoring and fraud detection
Optimize model performance through advanced fine-tuning techniques and reinforcement learning
Architect scalable LLM solutions across cloud and on-premises infrastructure
Navigate ethical challenges and regulatory requirements for responsible AI in finance
Get to grips with emerging technologies including multimodal AI systems for comprehensive financial analysis
Who this book is forIf you're a finance or technology professional looking to leverage AI and large language models in your work, this book is for you. It's especially useful for AI Engineers, quantitative analysts, fintech developers, and data scientists seeking practical guidance on applying LLMs in finance. If you already have a foundation in machine learning and basic finance, this book will help you advance to cutting-edge LLM applications. Basic familiarity with Python programming and machine learning is recommended to get the most out of this guide.
Contents
Table of Contents
Introduction to Large Language Models
Core Technologies and Implementations
Financial Agents
Financial Market Applications
Banking Applications
Financial Document Processing and Advisory
Reinforcement Learning in LLMs
Infrastructure and Performance
Ethics, Governance and Compliance
Future Developments
Technical Foundations
