Credit Risk Modeling in Python
bestseller

with Nikolay Georgiev
4.9/5
(398)

Blend credit risk modeling skills with Python programming: Learn how to estimate a bank’s loan portfolio's expected loss

8 hours of content 9570 students

$99.00

Lifetime access

Buy now
14-Day Money-Back Guarantee

What you get:

  • 8 hours of content
  • 80 Interactive exercises
  • 119 Downloadable resources
  • World-class instructor
  • Closed captions
  • Q&A support
  • Future course updates
  • Course exam
  • Certificate of achievement

Credit Risk Modeling in Python
bestseller

A course by Nikolay Georgiev

$99.00

Lifetime access

Buy now
14-Day Money-Back Guarantee

What you get:

  • 8 hours of content
  • 80 Interactive exercises
  • 119 Downloadable resources
  • World-class instructor
  • Closed captions
  • Q&A support
  • Future course updates
  • Course exam
  • Certificate of achievement

$99.00

Lifetime access

Buy now

$99.00

Lifetime access

Buy now
14-Day Money-Back Guarantee

What you get:

  • 8 hours of content
  • 80 Interactive exercises
  • 119 Downloadable resources
  • World-class instructor
  • Closed captions
  • Q&A support
  • Future course updates
  • Course exam
  • Certificate of achievement

What You Learn

  • Expand your business acumen with deep understanding of retail banking processes and identifying key value drivers
  • Gain comprehensive credit risk modeling knowledge, including concepts such as Basel II, probability of default (PD), loss given default (LGD), and exposure at default (EAD)
  • Apply logistic regression in Python to predict credit risk
  • Boost your data pre-processing skills by cleaning real-life loan portfolio data
  • Acquire specialized credit risk modeling skills and differentiate your data scientist resume
  • Secure a competitive edge over other candidates when applying for retail banking data scientist roles

Top Choice of Leading Companies Worldwide

Industry leaders and professionals globally rely on this top-rated course to enhance their skills.

Course Description

Credit risk modeling is the place where data science and fintech meet. It is one of the most important activities conducted in a bank, with the most attention since the recession. At present, it is the only comprehensive credit risk modeling course in Python available online – taking you from preprocessing, through probability of default (PD), loss given default (LGD) and exposure at default (EAD) modeling, all the way to calculating expected loss (EL).

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What does the course cover

1.1 What does the course cover

5 min

What is credit risk and why is it important?

1.2 What is credit risk and why is it important?

5 min

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

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

4 min

Capital adequacy, regulations, and the Basel II accord

1.4 Capital adequacy, regulations, and the Basel II accord

5 min

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

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

10 min

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

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

9 min

Interactive Exercises

Practice what you've learned with coding tasks, flashcards, fill in the blanks, multiple choice, and other fun exercises.

Practice what you've learned with coding tasks, flashcards, fill in the blanks, multiple choice, and other fun exercises.

categorize exercise
true-false exercise
single-choice exercise
multiple-choice exercise
flashcards exercise
fill-blank-typing exercise
fill-blank-selection exercise
order exercise

Curriculum

  • 1. Introduction
    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
    5 min
    What is credit risk and why is it important?
    5 min
    Expected loss (EL) and its components: PD, LGD and EAD
    4 min
    Capital adequacy, regulations, and the Basel II accord
    5 min
    Basel II approaches: SA, F-IRB, and A-IRB
    10 min
    Different facility types (asset classes) and credit risk modeling approaches
    9 min
  • 2. Setting up the environment
    2 Lessons 2 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 Read now
    1 min
    Installing the relevant packages
    1 min
  • 3. Dataset description
    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
    3 min
    Dependent variables and independent variables
    6 min
  • 4. General preprocessing
    6 Lessons 29 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
    4 min
    Preprocessing few continuous variables
    13 min
    Preprocessing few continuous variables Homework Read now
    1 min
    Preprocessing few discrete variables
    7 min
    Check for missing values and clean
    3 min
    Check for missing values and clean Homework Read now
    1 min
  • 5. PD model: data preparation
    18 Lessons 117 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?
    4 min
    Dependent variable: Good/ Bad (default) definition
    5 min
    Constructing independent variables
    6 min
    Information value
    5 min
    Data preparation. Splitting data
    8 min
    Data preparation. Preprocessing one discrete variable
    8 min
    Data preparation. Preprocessing discrete variables: automating calculations
    6 min
    Data preparation. Preprocessing discrete variables: visualizing results
    10 min
    Data Preparation. Preprocessing Discrete Variables: Creating Dummies (part 1)
    7 min
    Data Preparation. Preprocessing Discrete Variables: Creating Dummies (part 2)
    11 min
    Data Preparation. Preprocessing Discrete Variables: Creating Dummies (part 2) Read now
    1 min
    Data preparation. Preprocessing continuous variables: automating calculations
    5 min
    Data preparation. Preprocessing continuous variables: creating dummies (part 1)
    7 min
    Data preparation. Preprocessing continuous variables: creating dummies (part 2)
    14 min
    Data preparation. Preprocessing continuous variables: creating dummies (part 2) Read now
    2 min
    Creating dummies Homework Read now
    1 min
    Data preparation. Preprocessing continuous variables: creating dummies (part 3)
    13 min
    Data preparation. Preprocessing the test dataset
    4 min
  • 6. 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
    8 min
    Loading the data and selecting the features
    6 min
    PD model estimation
    4 min
    Build a logistic regression model with p-values.
    11 min
    Interpreting the coefficients in the PD model
    6 min
  • 7. 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).
    7 min
    Evaluation of model performance: accuracy and area under the curve (AUC)
    11 min
    Evaluation of model performance: Gini and Kolmogorov-Smirnov.
    10 min
  • 8. Applying the PD model for decision making
    6 Lessons 37 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
    5 min
    Creating a scorecard
    13 min
    Calculating credit score
    6 min
    From credit score to PD
    3 min
    Setting cut-offs
    9 min
    Setting cut-offs Homework Read now
    1 min
  • 9. PD model monitoring
    4 Lessons 29 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
    5 min
    Population stability index: preprocessing
    12 min
    Population stability index: calculation and interpretation
    11 min
    Population stability index: calculation and interpretation Homework Read now
    1 min
  • 10. 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
    6 min
    LGD and EAD models: dependent variables
    5 min
    LGD and EAD models: distribution of recovery rates and credit conversion factors
    6 min
  • 11. LGD model
    8 Lessons 29 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
    3 min
    LGD model: testing the model
    5 min
    LGD model: estimating the accuracy of the model
    5 min
    LGD model: saving the model
    3 min
    LGD model: stage 2 – linear regression
    4 min
    LGD model: stage 2 – linear regression evaluation
    4 min
    LGD model: combining stage 1 and stage 2
    3 min
    LGD model: combining stage 1 and stage 2 Homework Read now
    2 min
  • 12. EAD model
    3 Lessons 11 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
    6 min
    EAD model validation Read now
    1 min
    EAD model validation
    4 min
  • 13. Calculating expected loss
    2 Lessons 17 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 Homework Read now
    1 min
    Calculating expected loss
    16 min
See More

Topics

TheoryPythonData AnalysisProgrammingCredit RiskLogistic RegressionData PreprocessingData AnalysisFinance SkillsIndustry Specialization

Tools & Technologies

python
theory

Course Requirements

  • You need to complete an introduction to Python before taking this course
  • Basic skills in statistics, probability, and linear algebra are required
  • It is highly recommended to take the Machine Learning in Python course first
  • You will need to install the Anaconda package, which includes Jupyter Notebook

Who Should Take This Course?

Level of difficulty: Advanced

  • Aspiring data scientists
  • Current data scientists who are passionate about acquiring domain-specific knowledge in credit risk modeling

Exams and Certification

A 365 Data Science Course Certificate is an excellent addition to your LinkedIn profile—demonstrating your expertise and willingness to go the extra mile to accomplish your goals.

Exams and certification

Meet Your Instructor

Nikolay Georgiev

Nikolay Georgiev

Director of Data Science and Automation at

2 Courses

781 Reviews

17346 Students

Nikolay is a Director of Data Science and Automation at KBC Group. He has a solid background in marketing analytics, risk modeling, and research. A Master’s degree in Science and a Ph.D. in Economics and Business Administration have given Nikolay vast experience in the academic world. He spent over six years in the field of research at HEC Paris, BI Norwegian Business School, and the University of Texas at Austin, U.S. In addition, Nikolay has worked on numerous projects for Coca-Cola Hellenic and Shawbrook Bank (UK) that involved building highly accurate quantitative models and solutions for customer portfolio management, credit risk, social media marketing research, and psychological targeting.

What Our Learners Say

Talib Hussain
Talib Hussain
26.02.2025
Well organized and extremely useful
Ibtissam
Ibtissam
24.01.2025

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