– 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 …
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