Deep Credit Risk: Machine Learning with Python

by Daniel Rösch, Harald Scheule

www.deepcreditrisk.com provides real credit data, apps and much more.

"Deep Credit Risk - Machine Learning with Python" aims at starters and pros alike to enable you
- Understand the role of liquidity, equity and many other key banking features
- Engineer and select features
- Predict defaults, payoffs, loss rates and exposures
- Predict downturn and crisis outcomes using pre-crisis features
- Understand the implications of COVID-19
- Apply innovative sampling techniques for model training and validation
- Deep-learn from Logit Classifiers to Random Forests and Neural Networks
- Do unsupervised Clustering, Principal Components and Bayesian Techniques
- Build multi-period models for CECL, IFRS 9 and CCAR
- Build credit portfolio correlation models for VaR and Expected Shortfall
- Run over 1,500 lines of pandas, statsmodels and scikit-learn Python code

473 pages, Paperback

Published June 24, 2020