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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Chapter 9. Fighting Bias

We like to think that machines are more rational than us: heartless silicon applying cold logic. Thus, when computer science introduced automated decision making into the economy, many hoped that computers would reduce prejudice and discrimination. Yet, as we mentioned earlier when looking at mortgage applications and ethnicity, computers are made and trained by humans, and the data that those machines use stems from an unjust world. Simply put, if we are not careful, our programs will amplify human biases.

In the financial industry, anti-discrimination is not only a matter of morality. Take, for instance, the Equal Credit Opportunity Act (ECOA), which came into force in 1974 in the United States. This law explicitly forbids creditors from discriminating applicants based on race, sex, marital status, and several other attributes. It also requires creditors to inform applicants about the reasons for denial.

The algorithms discussed in this book are discrimination machines...