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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A checklist for developing fair models

With the preceding information, we can create a short checklist that can be used when creating fair models. Each issue comes with several sub-issues.

What is the goal of the model developers?

  • Is fairness an explicit goal?

  • Is the model evaluation metric chosen to reflect the fairness of the model?

  • How do model developers get promoted and rewarded?

  • How does the model influence business results?

  • Would the model discriminate against the developer's demographic?

  • How diverse is the development team?

  • Who is responsible when things go wrong?

Is the data biased?

  • How was the data collected?

  • Are there statistical misrepresentations in the sample?

  • Are sample sizes for minorities adequate?

  • Are sensitive variables included?

  • Can sensitive variables be inferred from the data?

  • Are there interactions between features that might only affect subgroups?

Are errors biased?

  • What are the error rates for different subgroups?

  • What is the error rate of a simple, rule-based alternative?

  • How do the...