Machine Learning for Financial Risk Management

Financial risk management is quickly evolving with the help of artificial intelligence. With this practical book, developers, programmers, engineers, financial analysts, and risk analysts will explore Python-based machine learning and deep learning models for assessing financial risk. You’ll learn how to compare results from ML models with results obtained by traditional financial risk models.

Author Abdullah Karasan helps you explore the theory behind financial risk assessment before diving into the differences between traditional and ML models.

  • Review classical time series applications and compare them with deep learning models
  • Explore volatility modeling to measure degrees of risk, using support vector regression, neural networks, and deep learning
  • Revisit and improve market risk models (VaR and expected shortfall) using machine learning techniques
  • Develop a credit risk based on a clustering technique for risk bucketing, then apply Bayesian estimation, Markov chain, and other ML models
  • Capture different aspects of liquidity with a Gaussian mixture model
  • Use machine learning models for fraud detection
  • Identify corporate risk using the stock price crash metric
  • Explore a synthetic data generation process to employ in financial risk

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
task but at the same time it has been fiercely critized due mainly to lack of
flexibility and non-inclusiveness of these models. 2007-2008 financial crisis
fueled this debate and paved the way for innovations and different
approaches in the field of financial modeling.

Of course, this financial crisis is not the mere reason that precipitates the
growth of AI applications in finance but also two more main drivers have
spurred the adoption of AI in finance. That being said, data availability has
enhanced computing power and intensified researches in 1990s.

Financial Stability Board (2017) stresses the validity this fact by stating:
“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.”

—FSB


As a sub-branch of financial modeling, financial risk management has been
evolving with the adoption of AI in paralell with ever-growing role in
financial decision making process. In his celebrated book, Bostrom (2014)
denotes that there are two important revolutions in the history of mankind:
Agricultural Revolution and Industrial Revolution. These two revolutions
have such a profound impact that any third revolution of similar magnitude
would double in size of world economy in 2 weeks. Even more strikingly, if
the third revolution accomplishes by artificial intelligence, the impact would
be way more profound.

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