Credit Risk Modeling in Python

Curriculum

• Introduction Free

  6 Lessons 38 Min

  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 does the course cover Free

  What is credit risk and why is it important? Free

  Expected loss (EL) and its components: PD, LGD and EAD Free

  Capital adequacy, regulations, and the Basel II accord Free

  Basel II approaches: SA, F-IRB, and A-IRB Free

  Different facility types (asset classes) and credit risk modeling approaches Free
• Setting up the environment Free

  2 Lessons 1 Min

  Here you will learn how to set up Python 3 and load up Jupyter. We’ll also show you what the Anaconda Prompt is and how you can use it to download and import new modules.

  Setting up the environment Free

  Installing the relevant packages Free
• Dataset description Free

  2 Lessons 9 Min

  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 Free

  Dependent variables and independent variables Free
• General preprocessing Free

  4 Lessons 27 Min

  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 Free

  Preprocessing few continuous variables Free

  Preprocessing few discrete variables Free

  Check for missing values and clean Free
• PD model: data preparation

  15 Lessons 113 Min

  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

  Constructing independent variables

  Information value

  Data preparation. Splitting data

  Data preparation. Preprocessing one discrete variable

  Data preparation. Preprocessing discrete variables: automating calculations

  Data preparation. Preprocessing discrete variables: visualizing results

  Data Preparation. Preprocessing Discrete Variables: Creating Dummies (part 1)

  Data Preparation. Preprocessing Discrete Variables: Creating Dummies (part 2)

  Data preparation. Preprocessing continuous variables: automating calculations

  Data preparation. Preprocessing continuous variables: creating dummies (part 1)

  Data preparation. Preprocessing continuous variables: creating dummies (part 2)

  Data preparation. Preprocessing continuous variables: creating dummies (part 3)

  Data preparation. Preprocessing the test dataset
• PD model estimation

  5 Lessons 35 Min

  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
• PD model validation (test)

  3 Lessons 28 Min

  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.
• Applying the PD model for decision making

  5 Lessons 36 Min

  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
• PD model monitoring

  3 Lessons 28 Min

  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
• LGD and EAD models

  3 Lessons 17 Min

  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
• LGD model

  7 Lessons 27 Min

  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
• EAD model

  2 Lessons 10 Min

  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
• Calculating expected loss

  1 Lesson 16 Min

  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