Developing Credit Risk Scorecard Using R Programming

Name: Developing Credit Risk Scorecard Using R Programming
Rating: 4.4 (21 reviews)

Learn the entire process of credit risk scorecard development using R Programming with clear and concise instructions.

Created bySubhashish Ray

Last updated 10/2025

English

English [Auto],

What you'll learn

Understand the concept of credit risk and its significance in the financial industry.
Gain proficiency in using R programming for data manipulation, visualization, and statistical analysis.
Develop predictive models, such as logistic regression and decision trees, to assess credit risk effectively.
Interpret and communicate credit risk scores, providing actionable insights to stakeholders.
Demonstrate knowledge of feature engineering methods to create informative variables for credit risk modeling.

Course content

3 sections • 15 lectures • 2h 3m total length

Introduction2:17
Develop a credit risk scorecard in R programming by exploring the data frame, preparing data, splitting training and test sets, and evaluating performance with roc and auc.
Exploring the Dataset11:10
Explore a 20-variable credit dataset in RStudio, distinguishing independent attributes from the dependent loan performance (0 = bad, 1 = good) to lay groundwork for credit risk modeling.
Steps in Model Building7:39
Select and preprocess the bank credit scoring data, then build a classification model using training and test sets and evaluate predictions with metrics like confusion matrix and ROC AUC.
Data for Model Building3:53
Explore how to select data and predictor variables for a credit risk scorecard, using logistic regression to model the probability of default with borrower information, credit history, and behavioral data.

Preprocessing 110:08
Import the bank credit scoring data into R Studio, inspect 1000 observations across 21 variables, identify numeric and categorical types, and check for missing values and outliers.
Preprocessing 27:08
Preprocess the data by removing outliers and missing values, using imputation as needed, convert integers to factors, and keep only categorical features, yielding a 1000-observation, 18-variable credit_data_new.
Training and Test Datasets4:58
Split the credit data into training and testing sets in R using the sample command, with 70% for training and 30% for testing, to build and validate the model.

Model Development- Logistic Regression11:40
Develops a scorecard in R by building a logistic regression model with GLM to predict loan default from variables like account balance and loan purpose, using training and testing data.
Calculating Predicted Probabilities13:22
Develop a logistic regression model to predict default probabilities using five categorical variables, estimate coefficients via glm, and use predict to classify new applicants with a 0.6 threshold.
Model Fitting10:02
Fit a logistic regression model to predict default probabilities, then classify new borrowers with a 0.6 threshold and convert results to a binary factor for evaluation and plotting.
Creating Prediction and Performance Objects8:50
Create prediction and performance objects in R using the OCR package to evaluate a logistic regression model, plot ROC curves, and assess accuracy and area under the curve.
Confusion Matrix9:57
Explore the confusion matrix as a tool to evaluate a classification model, detailing true positives, true negatives, false positives, false negatives, and metrics like accuracy, recall, precision, and f-score.
Performance measure: AUC and ROC Curve5:40
Evaluate a credit risk scorecard with AUC and ROC curves to assess model performance, interpreting sensitivity and specificity for separating good and bad borrowers.
Performance Measure Calculation in R studio: AUC and ROC Curve7:37
Plot ROC curves in R studio, with FPR on the x-axis and TPR on the y-axis, and compute AUC to assess a credit risk scorecard; higher AUC indicates better discrimination.
Calculating Model Accuracy and Analyzing Confusion Matrix8:54
Compute the model's overall accuracy using acc dot perf and the performance function, then use the caret package to generate a confusion matrix revealing sensitivity, specificity, and more.
Evaluating Credit Risk Models

Requirements

Computer and Software Requirements: Participants must have access to a computer with R and RStudio installed. RStudio is a popular integrated development environment (IDE) for R, providing a user-friendly interface and enhanced functionalities for data analysis.
Eagerness to Learn and Engage: A positive attitude and eagerness to learn are essential for getting the most out of this course. Active engagement with the course material, participation in exercises, and asking questions will enhance your learning experience.
Time Commitment: The course duration and time commitment may vary depending on the depth and intensity of the material. Ensure you can dedicate sufficient time to complete the course and practice the concepts covered.

Description

The "Developing Credit Risk Scorecard using R Programming" course is designed to equip participants with the necessary knowledge and skills to build robust credit risk scorecards using the R programming language. Credit risk scorecards are vital tools used by financial institutions to assess the creditworthiness of borrowers and make informed lending decisions. This course will take participants through the entire process of developing a credit risk scorecard, from data preprocessing and feature engineering to model development, validation, and deployment.

Course Objectives: By the end of this course, participants will:

Understand the fundamentals of credit risk assessment and the role of scorecards in the lending process.
Be proficient in using R programming for data manipulation, visualization, and statistical analysis.
Learn how to preprocess raw credit data and handle missing values, outliers, and data imbalances.
Master various feature engineering techniques to create informative variables for credit risk modeling.
Gain hands-on experience in building and optimizing predictive models for credit risk evaluation.
Learn how to validate credit risk scorecards using appropriate techniques to ensure accuracy and reliability.
Understand the best practices for scorecard implementation and monitoring.

Target Audience: This course is ideal for data analysts, risk analysts, credit risk professionals, and anyone interested in building credit risk scorecards using R programming.

Note: Participants should have access to a computer with R and RStudio installed to fully engage in the hands-on exercises and projects throughout the course.

Who this course is for:

Data Analysts and Data Scientists: Professionals working with data who want to specialize in credit risk analysis and scorecard development using R programming.
Risk Analysts and Credit Risk Managers: Individuals involved in risk assessment, risk modeling, and credit decision-making in banks, lending institutions, or financial organizations.
Financial Analysts and Researchers: Individuals looking to gain a deeper understanding of credit risk assessment and scorecard development for research or analytical purposes.
Students and Academicians: Students pursuing degrees or conducting research in finance, risk management, or related fields can benefit from learning credit risk modeling using R.
Professionals Transitioning into Credit Risk: Those seeking a career change into the credit risk domain can use this course to develop relevant skills and knowledge.
Anyone Interested in Credit Risk Modeling: If you have a general interest in credit risk assessment and want to enhance your data analysis and modeling skills using R, this course can be a valuable learning opportunity.

Developing Credit Risk Scorecard Using R Programming

What you'll learn

Explore related topics

Course content

Introduction4 lectures • 25min

Data Preprocessing3 lectures • 22min

Model Development and Accuracy Check8 lectures • 1hr 16min

Requirements

Description

Who this course is for: