Credit Risk Modeling in Python

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

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!

• 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.
• 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.
• 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.
• 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.