Book Image

Machine Learning for Finance

By : Jannes Klaas
Book Image

Machine Learning for Finance

By: Jannes Klaas

Overview of this book

Machine Learning for Finance explores new advances in machine learning and shows how they can be applied across the financial sector, including insurance, transactions, and lending. This book explains the concepts and algorithms behind the main machine learning techniques and provides example Python code for implementing the models yourself. The book is based on Jannes Klaas’ experience of running machine learning training courses for financial professionals. Rather than providing ready-made financial algorithms, the book focuses on advanced machine learning concepts and ideas that can be applied in a wide variety of ways. The book systematically explains how machine learning works on structured data, text, images, and time series. You'll cover generative adversarial learning, reinforcement learning, debugging, and launching machine learning products. Later chapters will discuss how to fight bias in machine learning. The book ends with an exploration of Bayesian inference and probabilistic programming.
Table of Contents (15 chapters)
Machine Learning for Finance
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In this chapter, you have learned about fairness in machine learning in different aspects. First, we discussed legal definitions of fairness and quantitative ways to measure these definitions. We then discussed technical methods to train models to meet fairness criteria. We also discussed causal models. We learned about SHAP as a powerful tool to interpret models and find unfairness in a model. Finally, we learned how fairness is a complex systems issue and how lessons from complex systems management can be applied to make models fair.

There is no guarantee that following all the steps outlined here will make your model fair, but these tools vastly increase your chances of creating a fair model. Remember that models in finance operate in high-stakes environments and need to meet many regulatory demands. If you fail to do so, damage could be severe.

In the next, and final, chapter of this book, we will be looking at probabilistic programming and Bayesian inference.