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 data

The dataset we will work with is a synthetic dataset of transactions generated by a payment simulator. The goal of this case study and the focus of this chapter is to find fraudulent transactions within a dataset, a classic machine learning problem many financial institutions deal with.


Note: Before we go further, a digital copy of the code, as well as an interactive notebook for this chapter are accessible online, via the following two links:

An interactive notebook containing the code for this chapter can be found under

The code can also be found on GitHub, in this book's repository:

The dataset we're using stems from the paper PaySim: A financial mobile money simulator for fraud detection, by E. A. Lopez-Rojas, A. Elmir, and S. Axelsson. The dataset can be found on Kaggle under this URL:

Before we break it down...