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Credit Risk Modeling in Python

IntermedioLivello di competenza

Aggiornato 03/2026

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PythonApplied Finance

4 h

15 video

57 Esercizi

4,850 XP

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Descrizione del corso

Se hai mai richiesto una carta di credito o un prestito, sai che le società finanziarie analizzano le tue informazioni prima di decidere. Questo perché concedere un prestito può avere un impatto economico importante sul loro business. Ma come prendono la decisione? In questo corso imparerai a preparare i dati delle richieste di credito. Poi applicherai Machine Learning e regole di business per ridurre il rischio e assicurare la redditività. Userai due insiemi di dati che emulano reali richieste di credito, con un focus sul valore per il business. Unisciti a me e scopri il valore atteso del credit risk modeling!

Prerequisiti

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

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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

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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

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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

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Credit Risk Modeling in Python

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