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Course

Credit Risk Modeling in Python

IntermediateSkill Level

4.7+

Updated 03/2026

Learn how to prepare credit application data, apply machine learning and business rules to reduce risk and ensure profitability.

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PythonApplied Finance4 hr15 videos57 Exercises4,850 XP25,712Statement of Accomplishment

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Course Description

If you've ever applied for a credit card or loan, you know that financial firms process your information before making a decision. This is because giving you a loan can have a serious financial impact on their business. But how do they make a decision? In this course, you will learn how to prepare credit application data. After that, you will apply machine learning and business rules to reduce risk and ensure profitability. You will use two data sets that emulate real credit applications while focusing on business value. Join me and learn the expected value of credit risk modeling!

Prerequisites

Intermediate Python for Finance

1

Exploring and Preparing Loan Data

In this first chapter, we will discuss the concept of credit risk and define how it is calculated. Using cross tables and plots, we will explore a real-world data set. Before applying machine learning, we will process this data by finding and resolving problems.

Understanding credit risk

Explore the credit data

Crosstab and pivot tables

Outliers in credit data

Finding outliers with cross tables

Visualizing credit outliers

Risk with missing data in loan data

Replacing missing credit data

Removing missing data

Missing data intuition

2

Logistic Regression for Defaults

With the loan data fully prepared, we will discuss the logistic regression model which is a standard in risk modeling. We will understand the components of this model as well as how to score its performance. Once we've created predictions, we can explore the financial impact of utilizing this model.

Logistic regression for probability of default

Logistic regression basics

Multivariate logistic regression

Creating training and test sets

Predicting the probability of default

Changing coefficients

One-hot encoding credit data

Predicting probability of default

Credit model performance

Default classification reporting

Selecting report metrics

Visually scoring credit models

Model discrimination and impact

Thresholds and confusion matrices

How thresholds affect performance

Threshold selection

3

Gradient Boosted Trees Using XGBoost

Decision trees are another standard credit risk model. We will go beyond decision trees by using the trendy XGBoost package in Python to create gradient boosted trees. After developing sophisticated models, we will stress test their performance and discuss column selection in unbalanced data.

Gradient boosted trees with XGBoost

Trees for defaults

Gradient boosted portfolio performance

Assessing gradient boosted trees

Column selection for credit risk

Column importance and default prediction

Visualizing column importance

Column selection and model performance

Cross validation for credit models

Cross validating credit models

Limits to cross-validation testing

Cross-validation scoring

Class imbalance in loan data

Undersampling training data

Undersampled tree performance

Undersampling intuition

4

Model Evaluation and Implementation

After developing and testing two powerful machine learning models, we use key performance metrics to compare them. Using advanced model selection techniques specifically for financial modeling, we will select one model. With that model, we will: develop a business strategy, estimate portfolio value, and minimize expected loss.

Model evaluation and implementation

Comparing model reports

Comparing with ROCs

Calibration curves

Credit acceptance rates

Acceptance rates

Visualizing quantiles of acceptance

Acceptance rate impact

Credit strategy and minimum expected loss

Making the strategy table

Visualizing the strategy

Estimated value profiling

Total expected loss

Course wrap up

Credit Risk Modeling in Python

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This course is really easy to follow, wonderfully structured and explains every stage, I feel confident to apply this knowledge to real world data.

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FAQs

What machine learning models are used for credit risk in this course?

You will build logistic regression models and gradient boosted trees using XGBoost, then compare them using performance metrics to select the best model for credit decisions.

Does the course cover the business impact of credit risk models?

Yes. The final chapter covers developing a business strategy, estimating portfolio value, and minimizing expected loss based on your model's predictions.

What data preparation skills are covered?

Chapter 1 teaches you to explore credit application data using cross tables and plots, then find and resolve data quality problems before applying machine learning.

What Python prerequisites are needed?

You need Introduction to Python for Finance and Intermediate Python for Finance. This is a beginner-level applied finance course but assumes basic Python proficiency.

How does this course handle imbalanced data in credit applications?

Chapter 3 covers column selection techniques for unbalanced datasets and stress-tests model performance, which is critical since loan defaults are typically rare events.

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