Deep Learning in Banking

Unlock the potential of artificial intelligence to transform financial services for a new era

Deep Learning in Banking; Leveraging Artificial Intelligence for Next-Generation Financial Services by Cristián Bravo, Sebastián Maldonado, and María Óskarsdóttir is a compelling resource that highlights the critical intersection of AI and banking. It offers actionable insights and practical solutions for leveraging deep learning in lending institutions. With increasing regulatory challenges and the need for sophisticated models, this book provides essential strategies to navigate the evolving landscape of financial services.

Structured for both academic and professional use, Deep Learning in Banking delivers a comprehensive examination of the methodological frameworks of AI applications in lending. You'll learn to combine images, text, time series, graphs and structured data to develop multimodal deep learning and large-scale models, and how they relate to explainability and fairness, with practical examples and real-world case studies that ensure effective implementation.

Inside the book:

• Learn how to develop AI models within the modern regulatory environment.
• Explore multimodal data to develop deep learning models for financial institutions
• Discover case studies highlighting the application of advanced machine learning techniques in banking

Deep Learning in Banking is written for academics, financiers, banking professionals, and data scientists eager to revolutionize their approach to financial services. The book empowers its readers with the knowledge and tools needed to harness AI's full potential, paving the way for innovative and compliant solutions in the banking industry.

Contents

List of Figures

Foreword

Preface

Acknowledgments

Acronyms

Chapter 1: Introduction

Chapter 2: Image Processing and Convolutional Neural Networks

Chapter 3: Time Series and Panel Data in Banking

Chapter 4: Text Data and Transformers

Chapter 5: Financial Contagion and Network Models

Chapter 6: Generative AI and Large Language Models

Chapter 7: Multimodel Data and Information Fusion

Chapter 8: Fairness, Accountability, Explainability, and Causality

Chapter 9: Perspectives on the Future of AI in Banking

Bibliography

About the Authors

Index