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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Variational autoencoders

Autoencoders are basically an approximation for PCA. However, they can be extended to become generative models. Given an input, variational autoencoders (VAEs) can create encoding distributions. This means that for a fraud case, the encoder would produce a distribution of possible encodings that all represent the most important characteristics of the transaction. The decoder would then turn all of the encodings back into the original transaction.

This is useful since it allows us to generate data about transactions. One problem of fraud detection that we discovered earlier is that there are not all that many fraudulent transactions. Therefore, by using a VAE, we can sample any amount of transaction encodings and train our classifier with more fraudulent transaction data.

So, how do VAEs do it? Instead of having just one compressed representation vector, a VAE has two: one for the mean encoding, , and one for the standard deviation of this encoding, :

VAE scheme