Credit Risk Modeling in Python
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).
6 hours 
52 exercises 
1 assignments 
46 quizzes 
106 downloadables
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Section 1
Introduction
We start by explaining why credit risk is important for financial institutions. We also define ground 0 terms, such as expected loss, probability of default, loss given default and exposure at default.
What is credit risk and why is it important?
Expected loss (EL) and its components: PD, LGD and EAD
Capital adequacy, regulations, and the Basel II accord
Basel II approaches: SA, F-IRB, and A-IRB
Different facility types (asset classes) and credit risk modeling approaches
Section 2
Dataset description
Our example focuses on consumer loans. Since there are more than 100 potential features, we've devoted a complete section to explain why some features are chosen over others.
Our example: consumer loans. A first look at the dataset
Dependent variables and independent variables
Section 3
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.
Importing the data into Python
Preprocessing few continuous variables
Preprocessing few discrete variables
Check for missing values and clean
Section 4
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.
How is the PD model going to look like?
Dependent variable: Good/ Bad (default) definition
Fine classing, weight of evidence, coarse classing, information value
Data preparation. Splitting data
Data preparation. Preprocessing discrete variables: automating calculations
Data preparation. Preprocessing discrete variables: visualizing results
Data Preparation. Preprocessing Discrete Variables: Creating Dummies
Data preparation. Preprocessing continuous variables: automating calculations
Data preparation. Preprocessing continuous variables: creating dummies
Data preparation. Preprocessing the test dataset
Section 5
PD model estimation
Having set up all variables to be dummies, we estimate the probability of default. The most intuitive and widely accepted approach is to employ a logistic regression.
The PD model. Logistic regression with dummy variables
Loading the data and selecting the features
PD model estimation
Build a logistic regression model with p-values.
Interpreting the coefficients in the PD model
Section 6
PD model validation (test)
Since each model overfits the training data, it is crucial to test the results on out-of-sample observations. Consequently, we find its accuracy, its area under the curve (AUC), the Gini coefficient and the Kolmogorov-Smirnov test.
Out-of-sample validation (test).
Evaluation of model performance: accuracy and area under the curve (AUC)
Evaluation of model performance: Gini and Kolmogorov-Smirnov.
Section 7
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.
Calculating probability of default for a single customer
Creating a scorecard
Calculating credit score
From credit score to PD
Setting cut-offs
Section 8
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.
PD model monitoring via assessing population stability
Population stability index: preprocessing
Population stability index: calculation and interpretation
Section 9
LGD and EAD models
To calculate the final expected loss, we need three ingredients: probability of default (PD), loss given default (LGD) and exposure at default (EAD). In this section, we preprocess our data to be able to estimate the LGD and EAD models.
LGD and EAD models: independent variables
LGD and EAD models: dependent variables
LGD and EAD models: distribution of recovery rates and credit conversion factors
Section 10
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.
LGD model: preparing the inputs
LGD model: testing the model
LGD model: estimating the accuracy of the model
LGD model: saving the model
LGD model: stage 2 - linear regression
LGD model: stage 2 - linear regression evaluation
LGD model: combining stage 1 and stage 2
Section 11
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.
EAD model estimation and interpretation
EAD model validation
Section 12
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.
Calculating expected loss
Advanced Specialization
This course is part of Module 4 of the 365 Data Science Program. The complete training consists of four modules, each building upon your knowledge from the previous one. Module 4 is focused on developing a specialized, industry-relevant skill set, and students are encouraged to complete Modules 1, 2, and 3 before they start this part of the training. Here, you will learn how to perform Credit Risk Modeling for banks, Customer Analytics for retail or other commercial companies, and Time Series Analysis for finance and stock data.
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