Deep Learning in Banking: Integrating Artificial Intelligence for Next-Generation Financial Services

Authors

Cristian Bravo, Sebastian Maldonado, Maria Oskarsdottir

• ISBN: 9781394295371
• Published: December 2025
• Format: Hardcover
• Language: English
• Publisher: Wiley

Description

Deep Learning in Banking: Integrating Artificial Intelligence for Next-Generation Financial Services explores the growing intersection between artificial intelligence and modern banking. Written by Cristian Bravo, Sebastian Maldonado, and Maria Oskarsdottir, this book provides practical insights into how deep learning can be applied in lending institutions and across financial services, especially in a context of increasing regulatory complexity and demand for more sophisticated models.

Designed for both academic and professional use, the book examines methodological frameworks for AI applications in banking and explains how to combine images, text, time series, graphs, and structured data to build multimodal deep learning and large-scale models. It also addresses explainability, fairness, and implementation through practical examples and real-world case studies.

Key Features

- Explains how deep learning is transforming banking and financial services.

- Covers AI model development within modern regulatory environments.

- Explores multimodal data, including images, text, time series, graphs, and structured data.

- Discusses explainability and fairness in financial AI systems.

- Includes practical examples and case studies for real-world implementation.

- Suitable for both academic research and professional practice.

Coverage

This book covers artificial intelligence in banking, deep learning applications in lending institutions, multimodal model development, large-scale financial AI systems, explainability and fairness, regulatory considerations, and practical implementation in next-generation financial services.

About the Authors

Cristian Bravo, PhD, is a Professor and Canada Research Chair in Banking and Insurance Analytics at the University of Western Ontario, Canada, and Director of the Banking Analytics Lab. He is also co-author of Profit Driven Business Analytics and regularly appears as a panelist on CBC News’ Weekend Business Panel discussing banking, finance, and artificial intelligence.

Sebastian Maldonado, PhD, is Full Professor at the Department of Management Control and Information Systems, School of Economics and Business, University of Chile. He is the author of books on analytics, big data, and artificial intelligence applied to business.

Maria Oskarsdottir, is a Lecturer (Assistant Professor) of Mathematical Modelling and Data Science at the School of Mathematical Sciences at the University of Southampton and an Associate Professor at the Department of Computer Science of Reykjavik University. She is also an editor and associate editor for leading journals in machine learning and forecasting.

Table of Contents

The publisher flyer does not provide a full table of contents. Based on the publisher’s description, the book covers:

- AI and deep learning in banking

- Lending institutions and financial services applications

- Regulatory challenges in financial AI

- Multimodal deep learning using images, text, time series, graphs, and structured data

- Large-scale model development

- Explainability and fairness

- Real-world case studies and implementation strategies

Why buy this book?

This book is a strong choice for readers seeking a practical and forward-looking guide to artificial intelligence in banking. It combines academic depth with real-world relevance, helping professionals, researchers, and data scientists understand how deep learning can be implemented effectively and responsibly in financial services. Its focus on regulation, fairness, multimodal data, and case studies makes it especially valuable for institutions preparing for the next generation of financial innovation.

Keywords

deep learning, banking, artificial intelligence, financial services, lending institutions, machine learning, multimodal data, explainability, fairness, financial analytics, AI regulation, banking technology

Target Audience

banking professionals, financial analysts, data scientists, AI researchers, fintech professionals, academics, university libraries, business schools, finance departments

Genre

Banking, Finance, Artificial Intelligence, Data Science, Business, Technology

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