Harald Scheule & Daniel Rösch 
深度信用风险 (Deep Credit Risk) — 使用Python进行机器学习 [EPUB ebook] 

— 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 Va R and Expected Shortfal

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

— Access real credit data and much more …