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