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Course

Credit Risk Modeling in Python

Intermediate

4.8+

Updated 03/2025

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 hours15 videos57 Exercises4,850 XP21,988Statement 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

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

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

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

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

Course
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Included withPremium or Teams

Don’t just take our word for it

*4.8

from 42 reviews

81%

19%

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Most recent
Top reviews

Gaurav

about 4 hours

I really enjoyed this course! It breaks down the basics of credit risk modeling in a way that's easy to follow. The practical examples using real data helped me grasp concepts. If you're looking to dive into credit risk analysis, this is a great starting point.

Yun

about 16 hours

Aiqi

about 22 hours

Shivang

1 day

very informative course

Chayathorn

3 days

I have never been exposed to this before as a recent graduate. The course was inspiring and introduced me to tons of learnings.

Sahaya Princelin Gisha

4 days

"I really enjoyed this course! It breaks down the basics of credit risk modeling in a way that's easy to follow. The practical examples using real data helped me grasp concepts. If you're looking to dive into credit risk analysis, this is a great starting point."

Gaurav

Yun

Aiqi

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