Deep Learning in Banking
Integrating Artificial Intelligence for Next-Generation Financial Services
Structured for both academic and professional use, the book offers real-world case studies and actionable insights, covering trust, regulation, fairness, and explainability.
“A timely and outstanding contribution from three exceptional researchers, this book blends theoretical rigor with real-world applicability. With rich case studies and actionable insights, it’s an essential resource for navigating today’s data-driven banking environment.”
Bart Baesens, Professor of AI in Business, KU Leuven“Not just presenting models, but providing the critical framework for building trust in them. A must-read for anyone serious about managing next-generation risk and building intelligent financial systems.”
Dhagash Mehta, Head of Applied AI Research, Blackrock. Editorial Board Member at the Journal of Financial Data Science.
About Deep Learning in Banking
Deep Learning in Banking is a comprehensive resource at the intersection of artificial intelligence and financial services, combining academic depth with practical insight to address the real-world complexities of deploying deep learning technologies in banking.
The book explores advanced machine learning techniques, including convolutional neural networks, graph neural networks, transformers, and large language models, providing a deep dive into their application within the financial sector.
It also examines critical themes such as fairness, regulatory compliance, explainability, and trust. These considerations are central to developing responsible AI systems that meet the unique demands of the financial industry, ensuring ethical and robust implementation.
Designed for academics, practitioners, regulators, and graduate students alike, the book integrates real-world case studies drawn from financial applications with hands-on labs and open-source code to support experiential learning. It also provides a clear framework for understanding both the technical and ethical dimensions of artificial intelligence in finance. More than just a textbook, this is a forward-looking guide for building intelligent, transparent, and compliant financial systems.
Preorder Today!
Deep Learning in Banking
Real-Time Risk Analytics
Dynamic credit scoring visualization with neural network predictions
Image-Based Risk Modeling
Visualizing spatial data for credit risk using neural networks.
Time-Series Forecasting
Sequential modeling for predicting financial trends.
Financial Document NLP
Analyzing financial text with transformer models.
Graph-Based Loan Analysis
Exploring relationships in loan data with graph neural networks.
Multimodal Learning
Integrating text, image, and numerical data for comprehensive analysis.