- 了解流动性，房屋净值和许多其他关键银行业特征变量的作用；

- 选择并处理变量；

- 预测违约、偿付、损失率和风险敞口；

- 利用危机前特征预测经济衰退和危机后果；

- 理解COVID-19对信用风险带来的影响；

- 将创新的抽样技术应用于模型训练和验证；

- 从Logit分类器到随机森林和神经网络的深入学习;

- 进行无监督聚类、主成分和贝叶斯技术的应用；

- 为CECL、IFRS 9和CCAR建立多周期模型；

- 建立用于在险价值和期望损失的信贷组合相关模型；

- 使用更多真实的信用风险数据并运行超过1500行的代码...

- 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 ...

?????? (Deep Credit Risk) - ??Python?????? - pocket, Kinesisk, 2021