Deep Learning in Banking by Cristián Bravo, Sebastián Maldonado & María Óskarsdóttir

Deep Learning in Banking by Cristián Bravo, Sebastián Maldonado & María Óskarsdóttir

Deep Learning in Banking: Integrating Artificial Intelligence for Next-Generation Financial Services by Cristián Bravo, Sebastián Maldonado, and María Óskarsdóttir explores the intersection of deep learning, artificial intelligence, machine learning, data science, and modern banking. The book examines how advanced AI models can be applied to financial services while addressing important issues such as regulation, fairness, accountability, explainability, and responsible AI.

About This Book

Deep Learning in Banking provides a comprehensive look at how artificial intelligence is transforming the financial sector. Designed for both academic and professional readers, the book connects technical machine-learning methods with the practical realities of deploying AI systems in banking and financial services.

The authors explore a range of modern approaches, including convolutional neural networks, time-series models, transformers, network models, generative AI, large language models, and multimodal learning. These techniques are discussed in the context of real banking and finance applications, including risk management, credit-related problems, financial data analysis, and other financial services.

A major strength of the book is its focus on the challenges that accompany AI adoption in highly regulated financial environments. Topics such as fairness, accountability, explainability, causality, regulatory requirements, data use, and AI governance are incorporated alongside the technical material.

The book also covers generative AI and large language models in banking, including LLM applications, agentic AI, prompt engineering, fine-tuning, licensing considerations, and financial-sector use cases. Multimodal models and the combination of text, images, graphs, time series, and structured data are also explored.

With its combination of technical concepts, practical applications, and regulatory considerations, this book provides a valuable resource for understanding how AI can be developed and deployed responsibly in modern financial institutions.

Key Topics Covered

• Deep learning in banking
• Artificial intelligence in finance
• Machine learning
• Banking analytics
• Credit risk and risk management
• Convolutional neural networks
• Time-series models
• Transformers
• Large language models
• Generative AI
• Multimodal AI
• Financial network models
• Explainable AI
• AI fairness
• Accountability and governance
• Causal deep learning
• AI regulation
• Financial data science
• Next-generation financial services

Why Read This Book?

Deep Learning in Banking connects advanced AI techniques with real-world financial applications, making it useful for readers who want to understand both the technical and business dimensions of AI in banking.

The book is particularly valuable because it does not treat AI models in isolation. It also considers the regulatory, ethical, explainability, and operational challenges involved in putting these technologies into practice.

Who Should Read This?

Ideal for banking professionals, financial analysts, data scientists, AI and machine-learning professionals, fintech specialists, researchers, graduate students, academics, regulators, and finance professionals interested in artificial intelligence.

Product Details

• Title: Deep Learning in Banking
• Subtitle: Integrating Artificial Intelligence for Next-Generation Financial Services
• Authors: Cristián Bravo, Sebastián Maldonado & María Óskarsdóttir
• Language: English
• Edition: 1st Edition
• Publication: January 2026
• Pages: 336
• Publisher: Wiley
• ISBN: 9781394295371
• Genre: Technology / Finance / Artificial Intelligence
• Level: Intermediate to Advanced

Explore the future of AI-powered banking, financial machine learning, deep learning, generative AI, risk analytics, and responsible financial technology with Deep Learning in Banking.