Credit Risk Modeling in R Course

Name: Credit Risk Modeling in R
Rating: 4.815789473684211 (76 reviews)

Credit Risk Modeling in R

IntermediateSkill Level

4.8+

76 reviews

Updated 11/2023

Apply statistical modeling in a real-life setting using logistic regression and decision trees to model credit risk.

Create Your Free Account

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.

Course Description

This hands-on-course with real-life credit data will teach you how to model credit risk by using logistic regression and decision trees in R. Modeling credit risk for both personal and company loans is of major importance for banks. The probability that a debtor will default is a key component in getting to a measure for credit risk. While other models will be introduced in this course as well, you will learn about two model types that are often used in the credit scoring context; logistic regression and decision trees. You will learn how to use them in this particular context, and how these models are evaluated by banks.

Prerequisites

Intermediate R for Finance

Introduction and data preprocessing

This chapter begins with a general introduction to credit risk models. We'll explore a real-life data set, then preprocess the data set such that it's in the appropriate format before applying the credit risk models.

Introduction and data structure

50 XP

Exploring the credit data

100 XP

Interpreting a CrossTable()

50 XP

Histograms and outliers

50 XP

Histograms

100 XP

Outliers

100 XP

Missing data and coarse classification

50 XP

Deleting missing data

100 XP

Replacing missing data

100 XP

Keeping missing data

100 XP

Data splitting and confusion matrices

50 XP

Splitting the data set

100 XP

Creating a confusion matrix

100 XP

Start Chapter

Logistic regression

Logistic regression is still a widely used method in credit risk modeling. In this chapter, you will learn how to apply logistic regression models on credit data in R.

Logistic regression: introduction

50 XP

Basic logistic regression

100 XP

Interpreting the odds for a categorical variable

50 XP

Multiple variables in a logistic regression model

100 XP

Interpreting significance levels

50 XP

Logistic regression: predicting the probability of default

50 XP

Predicting the probability of default

100 XP

Making more discriminative models

100 XP

Evaluating the logistic regression model result

50 XP

Specifying a cut-off

100 XP

Comparing two cut-offs

50 XP

Wrap-up and remarks

50 XP

Comparing link functions for a given cut-off

100 XP

Start Chapter

Decision trees

Classification trees are another popular method in the world of credit risk modeling. In this chapter, you will learn how to build classification trees using credit data in R.

What is a decision tree?

50 XP

Computing the gain for a tree

100 XP

Changing one Gini...

50 XP

Building decision trees using the rpart()-package

50 XP

Undersampling the training set

100 XP

Changing the prior probabilities

100 XP

Including a loss matrix

100 XP

Pruning the decision tree

50 XP

Pruning the tree with changed prior probabilities

100 XP

Pruning the tree with the loss matrix

100 XP

Other tree options and the construction of confusion matrices

50 XP

One final tree using more options

100 XP

Confusion matrices and accuracy of our final trees

100 XP

Optimizing the accuracy

50 XP

Start Chapter

Evaluating a credit risk model

In this chapter, you'll learn how you can evaluate and compare the results obtained through several credit risk models.

Finding the right cut-off: the strategy curve

50 XP

Computing a bad rate given a fixed acceptance rate

100 XP

The strategy table and strategy curve

100 XP

To tree or not to tree?

50 XP

The ROC-curve

50 XP

ROC-curves for comparison of logistic regression models

100 XP

ROC-curves for comparison of tree-based models

100 XP

Input selection based on the AUC

50 XP

Another round of pruning based on AUC

100 XP

Best of four

50 XP

Further model reduction?

100 XP

Course wrap-up

50 XP

Start Chapter

Credit Risk Modeling in R

Course
Complete

Earn Statement of Accomplishment

Add this credential to your LinkedIn profile, resume, or CV
Share it on social media and in your performance reviewEnroll Now

Don’t just take our word for it

*4.8

from 76 reviews

84%

13%

Sort by

Delruba Mahmud

2 weeks ago

Takuya

4 weeks ago

Lea

4 weeks ago

Ana Lucia

6 weeks ago

Great

Zlatko

6 weeks ago

Kameron

2 months ago

Delruba Mahmud

Lea

"Great"

Ana Lucia

FAQs

What modeling techniques are used for credit risk in this course?

You learn logistic regression and decision trees, two widely used methods in credit scoring, and apply them to real-life credit data to predict borrower default probability.

Do I need prior finance knowledge to take this course?

No deep finance knowledge is required. The course introduces credit risk concepts from scratch, though you should have basic R skills from an introductory R for finance course.

Does this course use real credit data?

Yes. You work with real-life credit data throughout the course, from preprocessing through model building and evaluation, giving you practical experience with actual lending scenarios.

How are credit risk models evaluated in this course?

Chapter 4 teaches you how banks evaluate and compare credit risk models, helping you understand the performance metrics that matter in a lending context.

Who would benefit most from taking this course?

Aspiring risk analysts, data scientists in banking, and anyone interested in financial modeling will benefit. It provides foundational skills for credit scoring roles at financial institutions.

Join over 19 million learners and start Credit Risk Modeling in R today!

Create Your Free Account

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.

Credit Risk Modeling in R

Create Your Free Account

Training 2 or more people?

Course Description

Prerequisites

Introduction and data preprocessing

Logistic regression

Decision trees

Evaluating a credit risk model

Earn Statement of Accomplishment

Don’t just take our word for it

FAQs

What modeling techniques are used for credit risk in this course?

Do I need prior finance knowledge to take this course?

Does this course use real credit data?

How are credit risk models evaluated in this course?

Who would benefit most from taking this course?

Join over 19 million learners and start Credit Risk Modeling in R today!

Create Your Free Account

Grow your data skills with DataCamp for Mobile

Course Description

Earn Statement of Accomplishment

Don’t just take our word for it

FAQs

Do I need prior finance knowledge to take this course?

Does this course use real credit data?

How are credit risk models evaluated in this course?

Who would benefit most from taking this course?

Join over .css-nklxlk{color:var(--wf-brand--main, #03EF62);}19 million learners and start Credit Risk Modeling in R today!

Create Your Free Account

Grow your data skills with DataCamp for Mobile

Join over 19 million learners and start Credit Risk Modeling in R today!