深度信用风险 (Deep Credit Risk) - 使用Python进行机器学习

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- COVID-19

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- Logit;

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- CECLIFRS 9CCAR

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- 1500...

- 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 Shortfal

- Run over 1,500 lines of pandas, statsmodels and scikit-learn Python code

- Access real credit data and much more ...