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Credit Risk Modeling in Python

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Credit Analysis

Business Analytics & Intelligence

Credit Risk Modeling in Python

A complete data science case study: preprocessing, modeling, model validation and maintenance in Python

4.6

(7574)

34985 enrolled on this course

Last updated 11/2021

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₹ 3099.0

₹ 4599.0

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Lessons

Quizzes

Duration

13 Hours

Skill level

Beginner

Language

English

Certificate

Yes

Full lifetime access

Yes

Description

Hi! Welcome to Credit Risk Modeling in Python. This is the only online course that teaches you how banks use data science modeling in Python to improve their performance and comply with regulatory requirements. This is the perfect course for you, if you are interested in a data science career. Here’s why:
· The instructor is a proven expert, holding a PhD from the Norwegian Business school and having taught in world renowned universities such as HEC, the University of Texas, and the Norwegian Business school).
· The course is suitable for beginners. We start with theory and initial data pre-processing and gradually solve a complete exercise in front of you
· Everything we cover is up-to-date and relevant in today’s development of Python models for the banking industry
· This is the only online course that provides a complete picture of credit risk in Python (using state of the art techniques to model all three aspects of the expected loss equation - PD, LGD, and EAD) including creating a scorecard from scratch
· Here we show you how to create models that are compliant with Basel II and Basel III regulations that other courses rarely touch upon
· We are not going to work with fake data.

The dataset used in this course is an actual real-world example
· You get to differentiate your data science portfolio by showing skills that are highly demanded in the job marketplace
· What is most important – you get to see first-hand how a data science task is solved in the real-world
Most data science courses cover several frameworks but skip the pre-processing and theoretical part. This is like learning how to taste wine before being able to open a bottle of wine.
We don’t do that. Our goal is to help you build a solid foundation. We want you to study the theory, learn how to pre-process data that does not necessarily come in the ‘’friendliest’’ format, and of course, only then we will show you how to build a state of the art model and how to evaluate its effectiveness.
Throughout the course, we will cover several important data science techniques.
- 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
Along with the video lessons you will receive several valuable resources that will help you learn as much as possible:
· Lectures
· Notebook files
· Homework
· Quiz questions
· Slides
· Downloads
· Access to Q&A where you could reach out and contact the course tutor.
Signing up for the course today could be a great step towards your career in data science. Make sure that you take full advantage of this amazing opportunity!
See you on the inside!
Who this course is for:
You should take this course if you are a data science student interested in improving their skills
You should take this course if you want to specialize in credit risk modeling
The course is also ideal for beginners, as it starts from the fundamentals and gradually builds up your skills
This course is for you if you want a great career

What you'll learn

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

Requirements

You will need a copy of Adobe XD 2019 or above. A free trial can be downloaded from Adobe.
No previous design experience is needed.
No previous Adobe XD skills are needed.

Course Content

27 sections • 95 lectures

Expand All Sections

1-Introduction

1.1-What does the course cover

1.2-What is credit risk and why is it important?

1.3-What is credit risk and why is it important?

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

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

1.6-Capital adequacy, regulations, and the Basel II accord

1.7-Capital adequacy, regulations, and the Basel II accord

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

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

1.10-Different facility types (asset classes) and credit risk modeling approaches

1.11-Different facility types (asset classes) and credit risk modeling approaches

2-Setting up the working environment

2.1-Setting up the environment - Do not skip, please!

2.2-Why Python and why Jupyter

2.3-Installing Anaconda

2.4-Jupyter Dashboard - Part 1

2.5-Jupyter Dashboard - Part 2

2.6-Installing the sklearn package

3-Dataset description

3.1-Our example: consumer loans. A first look at the dataset

3.2-Our example: consumer loans. A first look at the dataset

3.3-Dependent variables and independent variables

3.4-Dependent variables and independent variables

4-General preprocessing

4.1-Importing the data into Python

4.2-Importing the data into Python

4.3-Preprocessing few continuous variables

4.4-Preprocessing few continuous variables

4.5-Preprocessing few continuous variables: Homework

4.6-Preprocessing few discrete variables

4.7-Preprocessing few discrete variables

4.8-Check for missing values and clean

4.9-Check for missing values and clean

4.10-Check for missing values and clean: Homework

5-PD Model: Data Preparation

5.1-How is the PD model going to look like?

5.2-How is the PD model going to look like?

5.3-Dependent variable: Good/ Bad (default) definition

5.4-Dependent variable: Good/ Bad (default) definition

5.5-Fine classing, weight of evidence, and coarse classing

5.6-Fine classing, weight of evidence, and coarse classing

5.7-Information value

5.8-Information value

5.9-Data preparation. Splitting data

5.10-Data preparation. Splitting data

5.11-Data preparation. An example

5.12-Data preparation. An example

5.13-Data preparation. Preprocessing discrete variables: automating calculations

5.14-Data preparation. Preprocessing discrete variables: automating calculations

5.15-Data preparation. Preprocessing discrete variables: visualizing results

5.16-Data preparation. Preprocessing discrete variables: creating dummies (Part 1)

5.17-Data preparation. Preprocessing discrete variables: creating dummies (Part 1)

5.18-Data preparation. Preprocessing discrete variables: creating dummies (Part 2)

5.19-Data preparation. Preprocessing discrete variables: creating dummies (Part 2)

5.20-Data preparation. Preprocessing discrete variables. Homework.

5.21-Data preparation. Preprocessing continuous variables: Automating calculations

5.22-Data preparation. Preprocessing continuous variables: Automating calculations

5.23-Data preparation. Preprocessing continuous variables: creating dummies (Part 1)

5.24-Data preparation. Preprocessing continuous variables: creating dummies (Part 1)

5.25-Data preparation. Preprocessing continuous variables: creating dummies (Part 2)

5.26-Data preparation. Preprocessing continuous variables: creating dummies (Part 2)

5.27-Data preparation. Preprocessing continuous variables: creating dummies. Homework

5.28-Data preparation. Preprocessing continuous variables: creating dummies (Part 3)

5.29-Data preparation. Preprocessing continuous variables: creating dummies (Part 3)

5.30-Data preparation. Preprocessing continuous variables: creating dummies. Homework

5.31-Data preparation. Preprocessing the test dataset

5.32-PD model: data preparation notebooks

6-PD model estimation

6.1-The PD model. Logistic regression with dummy variables

6.2-The PD model. Logistic regression with dummy variables

6.3-Loading the data and selecting the features

6.4-PD model estimation

6.5-Build a logistic regression model with p-values

6.6-Build a logistic regression model with p-values

6.7-Interpreting the coefficients in the PD model

6.8-Interpreting the coefficients in the PD model

7-PD model validation

7.1-Out-of-sample validation (test)

7.2-Out-of-sample validation (test)

7.3-Evaluation of model performance: accuracy and area under the curve (AUC)

7.4-Evaluation of model performance: accuracy and area under the curve (AUC)

7.5-Evaluation of model performance: Gini and Kolmogorov-Smirnov

7.6-Evaluation of model performance: Gini and Kolmogorov-Smirnov

8-Applying the PD Model for decision making

8.1-Calculating probability of default for a single customer

8.2-Creating a scorecard

8.3-Creating a scorecard

8.4-Calculating credit score

8.5-Calculating credit score

8.6-From credit score to PD

8.7-From credit score to PD

8.8-Setting cut-offs

8.9-Setting cut-offs

8.10-Setting cut-offs. Homework

8.11-PD model: logistic regression notebooks

9-PD model monitoring

9.1-PD model monitoring via assessing population stability

9.2-PD model monitoring via assessing population stability

9.3-Population stability index: preprocessing

9.4-Population stability index: calculation and interpretation

9.5-Population stability index: calculation and interpretation

9.6-Homework: building an updated PD model

10-LGD and EAD Models: Preparing the data

10.1-LGD and EAD models: independent variables.

10.2-LGD and EAD models: independent variables

10.3-LGD and EAD models: dependent variables

10.4-LGD and EAD models: dependent variables

10.5-LGD and EAD models: distribution of recovery rates and credit conversion factors

10.6-LGD and EAD models: distribution of recovery rates and credit conversion factors

11-LGD model

11.1-LGD model: preparing the inputs

11.2-LGD model: testing the model

11.3-LGD model: testing the model

11.4-LGD model: estimating the accuracy of the model

11.5-LGD model: saving the model

11.6-LGD model: stage 2 – linear regression

11.7-LGD model: stage 2 – linear regression with comments

11.8-LGD model: stage 2 – linear regression evaluation

11.9-LGD model: stage 2 – linear regression evaluation

11.10-LGD model: combining stage 1 and stage 2

11.11-LGD model: combining stage 1 and stage 2

11.12-Homework: building an updated LGD model

12-EAD model

12.1-EAD model estimation and interpretation

12.2-EAD model estimation and interpretation

12.3-EAD model validation

12.4-EAD model validation

12.5-Homework: building an updated EAD model

13-Calculating expected loss

13.1-Calculating expected loss

13.2-Calculating expected loss

13.3-Homework: calculate expected loss on more recent data

13.4-Completing 100%

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