A compelling resource that highlights the critical intersection of AI and banking

Deep Learning in Banking: Leveraging Artificial Intelligence for Next-Generation Financial Services is a compelling resource that highlights the critical intersection of AI and banking. The book has just been published by John Wiley & Sons, and its authors include María Óskarsdóttir, associate professor at the Department of Computer Science at RU. María is also a Lecturer (Assistant Professor) of Mathematical Modelling and Data Science at the School of Mathematical Sciences at the University of Southampton.

Her co-authors, Cristián Bravo, PhD, is a Professor and the Canada Research Chair in Banking and Insurance Analytics at the University of Western Ontario, Canada, and Director of the Banking Analytics Lab. Sebastián Maldonado,, PhD, is a Full Professor at the Department of Management Control and Information Systems, School of Economics and Business, University of Chile.

They have collaborated for many years on research and the publication of articles related to banking. Through this collaboration, they repeatedly found a need for a shared reference source to which they could direct colleagues and students in the field.

The problem was clear: a reader interested in deep learning in banking had to choose from among numerous books on deep learning, search for relevant academic articles on artificial intelligence in banking, study the Basel regulatory framework and its national implementations, and locate specific regulatory documents on the use of unstructured data from institutions such as the Federal Reserve, the European Central Bank, and others. What was lacking was a single, comprehensive resource. We therefore decided to collaborate on writing a book that addresses the challenges of working with diverse data in banking, both from a practical perspective and within the regulatory framework. At the same time, the book provides the theoretical foundations of the models and demonstrates how they can be trained through hands-on exercises.

Explains María, adding that for most of her professional career, she has been fortunate to work on the implementation of a wide range of artificial intelligence solutions across diverse fields, including financial institutions, insurance companies, sleep research, telecommunications, and video games. She notes that the authors hope the book will become a key reference within financial institutions and educational settings, particularly in disciplines and study programmes that place an emphasis on data-driven risk management.

The book is structured for both academic and professional use, delivering 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.