Credit Risk Modeling in Python

Curriculum

• Introduction Free

  6 Lesson 38 Min
  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 Lesson 1 Min
  Setting up the environment Free Installing the relevant packages Free
• Dataset description Free

  2 Lesson 9 Min
  Our example: consumer loans. A first look at the dataset Free Dependent variables and independent variables Free
• General preprocessing Free

  4 Lesson 27 Min
  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 Lesson 113 Min
  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 Lesson 35 Min
  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 Lesson 28 Min
  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 Lesson 36 Min
  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 Lesson 28 Min
  PD model monitoring via assessing population stability Population stability index: preprocessing Population stability index: calculation and interpretation
• LGD and EAD models

  3 Lesson 17 Min
  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 Lesson 27 Min
  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 Lesson 10 Min
  EAD model estimation and interpretation EAD model validation
• Calculating expected loss

  1 Lesson 16 Min
  Calculating expected loss