Machine Learning for Financial Risk Management with Python

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 modelsExplore volatility modeling to measure degrees of risk, using support vector regression, neural networks, and deep learningRevisit and improve market risk models (VaR and expected shortfall) using machine learning techniquesDevelop a credit risk based on a clustering technique for risk bucketing, then apply Bayesian estimation, Markov chain, and other ML modelsCapture different aspects of liquidity with a Gaussian mixture modelUse machine learning models for fraud detectionIdentify corporate risk using the stock price crash metricExplore a synthetic data generation process to employ in financial risk

Abdullah Karasan was born in Berlin, Germany. After he studied Economics and Business Administration at Gazi University-Ankara, he obtained his master's degree from the University of Michigan-Ann Arbor and his PhD in Financial Mathematics from Middle East Technical University (METU)-Ankara. He worked as a Treasury Controller at the Undersecretariat of Treasury of Turkey. More recently, he has started to work as a Senior Data Science consultant and instructor for companies in Turkey and the USA. Currently, he is a Data Science consultant at Datajarlabs and Data Science mentor at Thinkful.

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