Author Description

"Deep Credit Risk: Machine Learning in R aims at starters and pros alike to enable you to: Understand the role of liquidity, equity and many other key banking features; Engineer and select features; Predict defaults, payoffs, loss rates 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 R code; Access real credit data and much more" --