Credit Risk Modeling in Python
6
Lessons56
Quizzes42
Assignments10
Course description
Credit risk modeling is the place where data science and fintech meet. It is one of the most important activities conducted in a bank and the one with the most attention since the recession. This course is the only comprehensive credit risk modeling course in Python available right now. It shows the complete credit risk modeling picture, from preprocessing, through probability of default (PD), loss given default (LGD) and exposure at default (EAD) modeling, and finally finishing off with calculating expected loss (EL).
General preprocessing
Each raw datasets has its drawbacks. While most preprocessing is model specific, in some cases (like missing values imputation), we could generalize the data preparation.
PD model: data preparation
Once we have completed all general preprocessing, we dive into model-specific preprocessing. We employ fine classing, coarse classing, weight of evidence and information value criterion to achieve the probability of default preprocessing. Conventionally, we should turn all variables into dummy indicators prior to modeling.
Applying the PD model for decision making
In practice, banks don't really want a complicated Python-implemented model. Instead, they prefer a simple score-card which contains only yes and no questions that could be employed by any bank employee. In this section, we learn how to create one.
PD model monitoring
Model estimation is extremely important, but an often-neglected step is model maintenance. A common approach is to monitor the population stability over time using the population stability index (PSI) and revisit our model if needed.
LGD model
LGD models are often estimated using a beta regression. To keep the modeling part simpler, we employ a two-step regression model, which aims to simulate a beta regression. We combine the predictions from a logistic regression with those from a linear regression to estimate the loss given default.
EAD model
The exposure at default (EAD) modeling is very similar to the LGD one. In this section, we take advantage of a linear regression to calculate EAD.
Calculating expected loss
After having calculated PD, LGD, and EAD, we reach the final step: computing expected loss (EL). This is also the number which is most interesting to C-level executives and is the finale of the credit risk modeling process.