Machine Learning for Financial Risk Management with Python

Preface

AI and ML reflect the natural evolution of technology as increased computing power enables computers to sort through large data sets and crunch numbers to identify patterns and outliers.

BlackRock (2019)

Financial modeling has a long history with many successfully accomplished tasks, but at the same time it has been fiercely criticized due mainly to lack of flexibility and non-inclusiveness of the models. The 2007–2008 financial crisis fueled this debate as well as paved the way for innovations and different approaches in the field of financial modeling.

Of course, the financial crisis was not the only thing precipitating the growth of AI applications in finance. Two other drivers, data availability and increased computing power, have spurred the adoption of AI in finance and have intensified research in this area starting in the 1990s.

The Financial Stability Board (2017) stresses the validity of this fact:

Many applications, or use “cases,” of AI and machine learning already exist. The adoption of these use cases has been driven by both supply factors, such as technological advances and the availability of financial sector data and infrastructure, and by demand factors, such as profitability needs, competition with other firms, and the demands of financial regulation.

As a subbranch of financial modeling, financial risk management has been evolving with the adoption of AI in parallel with its ever-growing role in the financial decision-making process. In his celebrated ...