Developing Credit Risk Scorecard Using R Programming

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

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.

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.

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 Exploring the Dataset

Lecture 3 Steps in Model Building

Lecture 4 Data for Model Building

Section 2: Data Preprocessing

Lecture 5 Preprocessing 1

Lecture 6 Preprocessing 2

Lecture 7 Training and Test Datasets

Section 3: Model Development and Accuracy Check

Lecture 8 Model Development- Logistic Regression

Lecture 9 Calculating Predicted Probabilities

Lecture 10 Model Fitting

Lecture 11 Creating Prediction and Performance Objects

Lecture 12 Confusion Matrix

Lecture 13 Performance measure: AUC and ROC Curve

Lecture 14 Performance Measure Calculation in R studio: AUC and ROC Curve

Lecture 15 Calculating Model Accuracy and Analyzing Confusion Matrix

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.