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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E2E modeling

Our current approach relies on engineered features. As we discussed at the start of this chapter, an alternative method is E2E modeling. In E2E modeling, both raw and unstructured data about a transaction is used. This could include the description text of a transfer, video feeds from cameras monitoring a cash machine, or other sources of data. E2E is often more successful than feature engineering, provided that you have enough data available.

To get valid results, and to successfully train the data with an E2E model it can take millions of examples. Yet, often this is the only way to gain an acceptable result, especially when it is hard to codify the rules for something. Humans can recognize things in images well, but it is hard to come up with exact rules that distinguish things, which is where E2E shines.

In the dataset used for this chapter, we do not have access to more data, but the rest of the chapters of this book demonstrate various E2E models.