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  3. Credit Risk Modeling in R
  • 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.

  • 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.

  • 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.

  • 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.

Initializing

Exercise

To tree or not to tree?

Imagine you have 2 banks: Bank A, and Bank B. These banks are restricted to using a certain acceptance rate.

  1. Bank A has to use an acceptance rate of 45%
  2. Bank B has to use an acceptance rate of 85%

Based on the strategy tables, which model will Bank A and Bank B prefer to use?

Do not hesitate to have a look at the tables again. They are loaded in your workspace as strategy_cloglog$table and strategy_loss_matrix$table.

Instructions

50 XP

Possible answers