Preface

In the last few years, we have seen spectacular growth in the field of data science. Almost every day there is some kind of new development, for example, a research paper announcing a new or improved machine learning or deep learning algorithm, or a new library for one of the most popular programming languages.

In the past, many of those advances did not make it to mainstream media. But that is also changing rapidly. Some of the recent examples include the AlphaGo program beating the 18-time world champion at Go, using deep learning to generate realistic faces of humans that never existed, or the beautiful digital art created from a text caption using models such as DALL-E 2 or Stable Diffusion.

Another example of recent and spectacular development is OpenAI’s ChatGPT. It is a language model with which we can engage in natural-sounding conversations. The model is able to keep track of past questions and follow up on them, admit its mistakes, or reject inappropriate requests. What is more, it is not only restricted to natural language, we can ask it to write actual code snippets in various programming languages.

Aside from those newsworthy achievements, in the last decades AI has been adopted in virtually every industry. We can see it all around us, for example, the recommendations we get on Netflix or the emails we receive about an extra discount from an online shop that we have not used recently. As such, businesses all over the world employ AI to gain a competitive edge in the following ways:

• Making better, data-driven decisions
• Increasing their profits by efficient targeting or spot-on recommendations
• Reducing customer churn by early identification of customers at risk
• Automating repetitive tasks that AI can complete much faster (and potentially more accurately) than a human employee

The very same AI revolution is affecting the financial industry. In a 2020 article, Forbes reported that “70% of all financial services firms are using machine learning to predict cash flow events, fine-tune credit scores and detect fraud”. Additionally, various aspects of data science are also used for algorithmic trading, robo-advisory services, personalized banking, process automation, and more.

This book presents a recipe-based guide on how to solve various tasks within the financial domain using modern Python libraries. As such, we try to reduce the amount of code that needs to be written by leveraging mature and “battle-tested” libraries used by professionals in many industries. While the book assumes some prior knowledge and does not explain all the concepts from the theoretical point of view, it provides relevant references that allow the readers to dive deeper into the topics.

In this preface, you will find an outline of what you can expect from the book, how the content is organized, and what you need to meet your goals while having hands-on fun on the way. I hope you will enjoy it!