Credit Risk Modeling in Python

Course Curriculum

Chapter 1: Introduction

Lecture 1: What does the course cover

Lecture 2: What is credit risk and why is it important?

Lecture 3: Expected loss (EL) and its components: PD, LGD and EAD

Lecture 4: Capital adequacy, regulations, and the Basel II accord

Lecture 5: Basel II approaches: SA, F-IRB, and A-IRB

Lecture 6: Different facility types (asset classes) and credit risk modeling approaches

Chapter 2: Setting up the working environment

Lecture 1: Setting up the environment – Do not skip, please!

Lecture 2: Why Python and why Jupyter

Lecture 3: Installing Anaconda

Lecture 4: Jupyter Dashboard – Part 1

Lecture 5: Jupyter Dashboard – Part 2

Lecture 6: Installing the sklearn package

Chapter 3: Dataset description

Lecture 1: Our example: consumer loans. A first look at the dataset

Lecture 2: Dependent variables and independent variables

Chapter 4: General preprocessing

Lecture 1: Importing the data into Python

Lecture 2: Preprocessing few continuous variables

Lecture 3: Preprocessing few continuous variables: Homework

Lecture 4: Preprocessing few discrete variables

Lecture 5: Check for missing values and clean

Lecture 6: Check for missing values and clean: Homework

Chapter 5: PD Model: Data Preparation

Lecture 1: How is the PD model going to look like?

Lecture 2: Dependent variable: Good/ Bad (default) definition

Lecture 3: Fine classing, weight of evidence, and coarse classing

Lecture 4: Information value

Lecture 5: Data preparation. Splitting data

Lecture 6: Data preparation. An example

Lecture 7: Data preparation. Preprocessing discrete variables: automating calculations

Lecture 8: Data preparation. Preprocessing discrete variables: visualizing results

Lecture 9: Data preparation. Preprocessing discrete variables: creating dummies (Part 1)

Lecture 10: Data preparation. Preprocessing discrete variables: creating dummies (Part 2)

Lecture 11: Data preparation. Preprocessing discrete variables. Homework.

Lecture 12: Data preparation. Preprocessing continuous variables: Automating calculations

Lecture 13: Data preparation. Preprocessing continuous variables: creating dummies (Part 1)

Lecture 14: Data preparation. Preprocessing continuous variables: creating dummies (Part 2)

Lecture 15: Data preparation. Preprocessing continuous variables: creating dummies. Homework

Lecture 16: Data preparation. Preprocessing continuous variables: creating dummies (Part 3)

Lecture 17: Data preparation. Preprocessing continuous variables: creating dummies. Homework

Lecture 18: Data preparation. Preprocessing the test dataset

Lecture 19: PD model: data preparation notebooks

Chapter 6: PD model estimation

Lecture 1: The PD model. Logistic regression with dummy variables

Lecture 2: Loading the data and selecting the features

Lecture 3: PD model estimation

Lecture 4: Build a logistic regression model with p-values

Lecture 5: Interpreting the coefficients in the PD model

Chapter 7: PD model validation

Lecture 1: Out-of-sample validation (test)

Lecture 2: Evaluation of model performance: accuracy and area under the curve (AUC)

Lecture 3: Evaluation of model performance: Gini and Kolmogorov-Smirnov

Chapter 8: Applying the PD Model for decision making

Lecture 1: Calculating probability of default for a single customer

Lecture 2: Creating a scorecard

Lecture 3: Calculating credit score

Lecture 4: From credit score to PD

Lecture 5: Setting cut-offs

Lecture 6: Setting cut-offs. Homework

Lecture 7: PD model: logistic regression notebooks

Chapter 9: PD model monitoring

Lecture 1: PD model monitoring via assessing population stability

Lecture 2: Population stability index: preprocessing