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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The feature engineering approach

The objective of feature engineering is to exploit the qualitative insight of humans in order to create better machine learning models. A human engineer usually uses three types of insight: intuition, expert domain knowledge, and statistical analysis. Quite often, it's possible to come up with features for a problem just from intuition.

As an example, in our fraud case, it seems intuitive that fraudsters will create new accounts for their fraudulent schemes and won't be using the same bank account that they pay for their groceries with.

Domain experts are able to use their extensive knowledge of a problem in order to come up with other such examples of intuition. They'll know more about how fraudsters behave and can craft features that indicate such behavior. All of these intuitions are then usually confirmed by statistical analysis, something that can even be used to open the possibilities of discovering new features.

Statistical analysis can sometimes turn...