Generative AI for Financial Services Challenges, anti–patterns, and best practices

Generative AI for Financial Services is a book targeted at any AI practitioner who wants to understand how generative AI works on a fundamental level and extend that learning to risks, challenges, and opportunities in regulated industries like financial services.
This book explores generative AI and its applications in financial services. It starts by explaining key concepts, differences from traditional AI, and the generative AI lifecycle. Readers learn about foundation models, transfer learning, and implementation strategies, including fine-tuning and training models. Practical use cases like improving customer experiences, automating ESG analysis, and optimizing contact centers are covered. The book also addresses challenges like model bias, ethics, and compliance while focusing on building trustworthy AI with fairness and accountability. Finally, it highlights MLOps and FMOps for efficient AI model deployment and management in production environments.
By the end of this book, you will be well-equipped to navigate the exciting world of generative AI and confidently apply its transformative potential within the financial services landscape.
Key Features
● Evolve from a generative AI enthusiast to an advanced practitioner in generative AI.
● Learn effective ways of building generative AI solutions for financial services.
● Become familiar with the upcoming trends in generative AI space.
What you will learn
● Learn about popular use-cases, challenges, risks in financial services, and how to overcome those.
● Learn how to launch and scale generative AI applications in regulated environments like financial services, expanding from tens to thousands of use cases.
● Understand the operational aspects of AI, including MLOps and FMOps.
● Address challenges and ethical considerations in AI implementation.
Who this book is for
This book is suitable for generative AI engineers, prompt engineers, ML engineers, data scientists, AI/ML, and technical leaders in financial services.
Table of Contents
1. Introduction to Generative AI
2. Types of Foundation Models
3. Generative AI Implementation Approaches
4. Generative AI Use-cases in Financial Services
5. Generative AI Challenges for Financial Services
6. Generative AI Anti-Patterns for Financial Services
7. Architectural Considerations in Generative AI
8. Use-case: Improving Customer Experience with Generative AI
9. Use-case: Contact Center Insights with Generative AI
10. Use-case: Fine-tuning for Financial Services
11. Use case: Automating ESG Analysis Using Agents
12. Choosing the Right Implementation Approach
13. Increasing Trustworthiness in Generative AI
14. Building Foundation Model Operations Practices




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