Deep Learning in Banking

Chapter 1Introduction

Summary

After reading this chapter, you will be able to:

• Understand who this book is for.
• Understand the basic building blocks of a neural network.
• Understand the current relevant regulatory frameworks and their impact in deep learning in model development.

The world is complex and multifaceted. This is not a controversial opinion. In fact, nuance and care in the development of models is paramount in banking. For decades, borrower behavior has been modeled and managed through data science models of increasing complexity. In credit scoring, for example, we have moved from tree-based models in the 1970s to logistic regression dominating the landscape until very recently. Currently, however, there has been a shift toward tree ensembles such as XGBoost and Random Forests in jurisdictions that allow it. This last shift is relevant as it shows how banking, while slow to adapt, can move toward more advanced models when the profits are clear and the regulatory hurdles can be overcome.

This book is our vision on what we believe is the next evolution of banking: the use of alternative data, that is, data that go beyond simple tabular variables, into new models that can consider complex inputs in a transparent and explainable manner. The pages in this book discuss this in detail, trying to always solve the balancing act that we believe makes artificial intelligence (AI) in banking so interesting. It must arise from a specific business need and show that it ...