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Visualizations for Credit Modeling in R – Part I

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Kristian Larsen

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Economic data scientist

Visualization is a great way to get an overview of credit modeling. Typically, you will start by making data management and data cleaning, and after this, your credit modeling analysis will start with visualizations. This article is, therefore, the first part of a credit machine learning analysis with visualizations. The second part of the analysis will typically use logistic regression and ROC curves.

Introduction to Credit Modelling

Credit modelling is the best way for lenders to understand how likely a particular loan is to get repaid. In other words, it’s a tool to understand the credit of a borrower. This is especially important because this credit profile keeps changing with time and circumstances.

There are many different factors that affect a person’s credit situation. This makes assessing a borrower’s credit a highly complex task. With so much money riding on our ability to accurately estimate the credit of a borrower, credit modeling has come into the picture.

Credit modelling refers to the process of using data models to find out two important things. The first is the probability of the borrower defaulting on the loan. The second is the impact on the financials of the lender if this default occurs.

Financial institutions rely on credit models to determine the credit of potential borrowers. They make decisions on whether or not to sanction a loan, as well as on the interest rate of the loan based on the credit model validation.

As technology has progressed, new ways of modeling credit have emerged, including credit modelling using R. These include using the latest analytics and big data tools to model credit. Other factors like the evolution of economies and the subsequent emergence of different types of credit have also impacted how credit modelling is done.

Credit Modelling in R

Now let us start using R for Credit Modelling:

Library of R packages

In the following section we will use R for visualization of credit modelling. First we read the packages into the R library:

Load dataset and data management

#Data management packages
library(readr)
library(lubridate)
library(magrittr)
library(plyr)
library(dplyr)
library(gridExtra)
# Visualization packages
library(ggplot2)
library(plotly)
library(ggthemes)

Next it is time to read the dataset and do some data management. We use the lending club loan dataset:

Read the dataset into R library
loan <- read.csv(“/loan.csv”)
Data management of the dataset
loan$member_id <- as.factor(loan$member_id)
loan$grade <- as.factor(loan$grade)
loan$sub_grade <- as.factor(loan$sub_grade)
loan$home_ownership <- as.factor(loan$home_ownership)
loan$verification_status <- as.factor(loan$verification_status)
loan$loan_status <- as.factor(loan$loan_status)
loan$purpose <- as.factor(loan$purpose)

Visit DataScience+ Blog to download the complete code on data selection and data cleaning.

In the second part of this article, Kristian will demonstrate how to create the visualizations for credit modeling

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