Credit Risk Modeling in Python

About Course

Learn how banks use data science modeling in Python to improve their performance and comply with regulatory requirements. This free course is perfect for beginners interested in a data science career.

Taught by a proven expert with a PhD from the Norwegian Business School and experience teaching at world-renowned universities, this course covers everything you need to know about credit risk modeling in Python, from theory and data pre-processing to building a complete model.

Key features of this free course:

Covers all aspects of the expected loss equation (PD, LGD, and EAD)
Shows how to create models that comply with Basel II and Basel III regulations
Uses a real-world dataset
Provides valuable resources like lectures, notebooks, homework, quizzes, slides, downloads, and Q&A support.

This free course covers:

Weight of evidence
Information value
Fine classing
Coarse classing
Linear regression
Logistic regression
Area Under the Curve
Receiver Operating Characteristic Curve
Gini Coefficient
Kolmogorov-Smirnov
Assessing Population Stability
Maintaining a model

Start your journey towards a career in data science today! Enroll in this free course on Theetay.

This course is completely free and available on Theetay, a website that offers free access to top-rated online courses from platforms like Udemy, Udacity, Coursera, MasterClass, NearPeer, and more.

Improve your Python modeling skills
Differentiate your data science portfolio with a hot topic
Fill up your resume with in demand data science skills
Build a complete credit risk model in Python
Impress interviewers by showing practical knowledge
How to preprocess real data in Python
Learn credit risk modeling theory
Apply state of the art data science techniques
Solve a real-life data science task
Be able to evaluate the effectiveness of your model
Perform linear and logistic regressions in Python

Course Content

Introduction

A Message from the Professor
What does the course cover

05:46
What is credit risk and why is it important

04:43
Expected loss (EL) and its components PD LGD and EAD

04:12
Capital adequacy regulations and the Basel II accord

04:32
Basel II approaches SA F-IRB and A-IRB

09:32
Different facility types (asset classes) and credit risk modeling approaches

09:21

Setting up the working environment

Dataset description

General preprocessing

PD Model Data Preparation

How is the PD model going to look like

03:50
Dependent variable Good Bad (default) definition

05:18
Fine classing weight of evidence and coarse classing

06:24
Information value

04:59
Data preparation. Splitting data

08:27
Data preparation. An example

08:20
Data preparation. Preprocessing discrete variables automating calculations

05:56
Data preparation. Preprocessing discrete variables visualizing results

09:35
Data preparation. Preprocessing discrete variables creating dummies (Part 1)

07:12
Data preparation. Preprocessing discrete variables creating dummies (Part 2)

11:15
Data preparation. Preprocessing continuous variables Automating calculations

04:35
Data preparation. Preprocessing continuous variables creating dummies (Part 1)

07:20
Data preparation. Preprocessing continuous variables creating dummies (Part 2)

14:01
Data preparation. Preprocessing continuous variables creating dummies (Part 3)

12:31
Data preparation. Preprocessing the test dataset

04:11

PD model estimation

PD model validation

Applying the PD Model for decision making

PD model monitoring

LGD and EAD Models Preparing the data

LGD model

EAD model

Calculating expected loss

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About Course

What Will You Learn?

Course Content

Introduction

A Message from the Professor

What does the course cover

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

Setting up the working environment

Setting up the environment – Do not skip please!

Why Python and why Jupyter

Installing Anaconda

Jupyter Dashboard – Part 1

Jupyter Dashboard – Part 2

Installing the sklearn package

Dataset description

Our example consumer loans. A first look at the dataset

Dependent variables and independent variables

General preprocessing

Importing the data into Python

Preprocessing few continuous variables

Preprocessing few discrete variables

Check for missing values and clean

PD Model Data Preparation

How is the PD model going to look like

Dependent variable Good Bad (default) definition

Fine classing weight of evidence and coarse classing

Information value

Data preparation. Splitting data

Data preparation. An example

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

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

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

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

PD model monitoring via assessing population stability

Population stability index preprocessing

Population stability index calculation and interpretation

LGD and EAD Models Preparing the data

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

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

EAD model estimation and interpretation

EAD model validation

Calculating expected loss

Calculating expected loss

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