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Credit Risk Modeling in R

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

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4 Hours16 Videos52 Exercises42,989 Learners4000 XPApplied Finance TrackQuantitative Analyst Track

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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.

  1. 1

    Introduction and data preprocessing

    Free

    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.

    Play Chapter Now
    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
  2. 2

    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.

    Play Chapter Now
    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
  3. 3

    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.

    Play Chapter Now
    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
  4. 4

    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.

    Play Chapter Now
    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

In the following tracks

Applied FinanceQuantitative Analyst

Datasets

Loan Data Chapter 1Loan Data Chapter 2, 3 and 4

Prerequisites

Intermediate R for Finance
Lore Dirick Headshot

Lore Dirick

Director of Data Science Education at Flatiron School

Lore is a data scientist with expertise in applied finance. She obtained her PhD in Business Economics and Statistics at KU Leuven, Belgium. During her PhD, she collaborated with several banks working on advanced methods for the analysis of credit risk data. Lore formerly worked as a Data Science Curriculum Lead at DataCamp, and is and is now Director of Data Science Education at Flatiron School, a coding school with branches in 8 cities and online programs.
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