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Online Course
top-rated
Credit Risk Modeling in Python
with
Nikolay Georgiev
Blend credit risk modeling skills with Python programming: Learn how to estimate a bank’s loan portfolio's expected loss
9992 students already have enrolled
Skill level:
Advanced
Duration:
8 hours
-
Lessons (7 hours)
-
Practice exams (1.75 hours)
- Lessons (7 hours)
- Practice exams (1.75 hours)
CPE credits:
12
CPE stands for Continuing Professional Education and represents the mandatory credits a wide range of
professionals must earn to maintain their licenses and stay current with regulations and best
practices. One CPE credit typically equals 50 minutes of learning. For more details, visit NASBA's
official website: www.nasbaregistry.org
CPE stands for Continuing Professional Education and represents the mandatory credits a wide range of
professionals must earn to maintain their licenses and stay current with regulations and best
practices. One CPE credit typically equals 50 minutes of learning. For more details, visit NASBA's
official website: www.nasbaregistry.org
Accredited:
certificate
What You Learn
- Master retail banking value drivers and credit risk modeling fundamentals.
- Understand credit risk modeling concepts like PD, LGD, EAD, and Basel II.
- Apply logistic regression in Python for accurate credit risk prediction.
- Boost your data cleaning and processing skills with real-world loan data.
- Acquire specialized credit risk modeling skills to enhance your resume.
- Become invaluable for data scientist roles in the retail banking sector.
Topics & tools
theorypythondata analysisprogrammingcredit risklogistic regressiondata preprocessingfinance skillsindustry specialization
Your instructor
Data Science Leader | Business Impact with AI & Behavioral Insights
Track record
Bringing real-world expertise from leading global companies
Academic background
Doctorate (PhD), Economics & Business Administration
Media and recognition
- Author of 11 scientific publications
- Presenter at international conferences and colloquiums
- Silver medalist in multiple academic competitions (Mathematics & Econometrics)
Course OVERVIEW
Description
CPE Credits: 12
Field of Study: Information Technology
Delivery Method: QAS Self Study
Delivery Method: QAS Self Study
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).
Prerequisites
- Python (version 3.8 or later), pandas library, and a code editor or IDE (e.g., Jupyter Notebook, Spyder, or VS Code)
- Basic familiarity with Python programming is required.
- Familiarity with NumPy is helpful but not mandatory.
Advanced preparation
Curriculum
68 lessons
80 exercises
5 exams
- 2. Setting up the environment2 MinHere 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.
- 3. Dataset description9 MinOur 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.
- 4. General preprocessing29 MinEach 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 FreeExercise FreePreprocessing few continuous variables FreePreprocessing few continuous variables Homework FreeExercise FreePreprocessing few discrete variables FreeExercise FreeCheck for missing values and clean FreeCheck for missing values and clean Homework FreeExercise Free
- 5. PD model: data preparation117 MinOnce 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?ExerciseDependent variable: Good/ Bad (default) definitionExerciseConstructing independent variablesExerciseInformation valueExerciseData preparation. Splitting dataExerciseData preparation. Preprocessing one discrete variableExerciseData preparation. Preprocessing discrete variables: automating calculationsExerciseData preparation. Preprocessing discrete variables: visualizing resultsData Preparation. Preprocessing Discrete Variables: Creating Dummies (part 1)ExerciseData Preparation. Preprocessing Discrete Variables: Creating Dummies (part 2)Data Preparation. Preprocessing Discrete Variables: Creating Dummies (part 2)ExerciseData preparation. Preprocessing continuous variables: automating calculationsExerciseData preparation. Preprocessing continuous variables: creating dummies (part 1)ExerciseData preparation. Preprocessing continuous variables: creating dummies (part 2)Data preparation. Preprocessing continuous variables: creating dummies (part 2)ExerciseData preparation. Preprocessing continuous variables: creating dummies (part 3)Creating dummies HomeworkExerciseData preparation. Preprocessing the test datasetPractice exam
- 6. PD model estimation35 MinHaving 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.
- 7. PD model validation (test)28 MinSince 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.
- 8. Applying the PD model for decision making37 MinIn 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.
- 9. PD model monitoring29 MinModel 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.
- 10. LGD and EAD models17 MinTo 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.
- 11. LGD model29 MinLGD 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 inputsLGD model: testing the modelExerciseLGD model: estimating the accuracy of the modelLGD model: saving the modelLGD model: stage 2 – linear regressionExerciseLGD model: stage 2 – linear regression evaluationExerciseLGD model: combining stage 1 and stage 2LGD model: combining stage 1 and stage 2 HomeworkExercise
- 12. EAD model11 MinThe 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.
- 13. Calculating expected loss17 MinAfter 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.
- 14. Course exam75 Min
Free preview
1.1 What does the course cover
5 min
1.2 What is credit risk and why is it important?
5 min
1.3 Expected loss (EL) and its components: PD, LGD and EAD
4 min
1.4 Capital adequacy, regulations, and the Basel II accord
5 min
1.5 Basel II approaches: SA, F-IRB, and A-IRB
10 min
1.6 Different facility types (asset classes) and credit risk modeling approaches
9 min
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ACCREDITED certificates
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