In this tutorial, we will learn credit risk modeling in R using case studies. Specifically, we will use two case studies starting with a simpler one using which we will learn the methodology and important concepts and techniques.
Note: As a pre-requisite, it will be helpful to go through this tutorial first – Foundations of Credit Risk Modelling.
Case Study 1: German Credit
In the first case study, we will use a popular dataset called German Credit. Our objective in this case study is to determine the Probability of Default (PD). We will build a predictive model that takes as input the various aspects of the loan applicant and outputs the probability of default of the loan applicant. PD is one of the most highly used measures for calculating the credit score of the borrowers. PD is also the primary parameter used in calculating credit risk as per the internal ratings-based approach used by banks.
The German Credit dataset contains observations on 21 attributes for 1000 past applicants for credit. Each applicant was rated as “good credit” (700 cases) or “bad credit” (300 cases).
In this case study, we will perform all the steps involved in model building and along the way, we will also understand the entire spectrum of the predictive modeling landscape.
Case Study 2: LendingClub Data
In the second case study, we will build upon the knowledge we have gained in the first case study and apply it to a new data set which is more realistic in nature. We will use the loan data available from LendingClub’s website. LendingClub is a US peer-to-peer lending company which matches borrowers with investors willing to fund their loans. The loan dataset contains actual data of the loans extended by them in their business. The dataset is much larger in size compared to the German Credit data and also contains a lot more variables that we need to work on. This case study will give us a more real-life experience of what we can expect when we build a model in our role as a data scientist in a bank.