Credit Risk Modeling using R Programming

Credit Risk Modeling using R Programming, available at $49.99, has an average rating of 3.7, with 29 lectures, based on 52 reviews, and has 248 subscribers.

You will learn about Learn Model Development from scratch Understand step by step application of R codes Understand output interpretation and it's business logic Aligning Analytics with Business Requirements Impress interviewers by showing practical knowledge of credit risk model development Learn the most in demand skill This course is ideal for individuals who are Beginners/students or Experienced analytics professional or working professionals who want to shift towards risk modeling or Data science/machine learning enthusiasts who want to learn a new skill It is particularly useful for Beginners/students or Experienced analytics professional or working professionals who want to shift towards risk modeling or Data science/machine learning enthusiasts who want to learn a new skill.

Enroll now: Credit Risk Modeling using R Programming

Summary

Title: Credit Risk Modeling using R Programming

Price: $49.99

Average Rating: 3.7

Number of Lectures: 29

Number of Published Lectures: 29

Number of Curriculum Items: 29

Number of Published Curriculum Objects: 29

Original Price: ₹1,299

Quality Status: approved

Status: Live

What You Will Learn

Learn Model Development from scratch
Understand step by step application of R codes
Understand output interpretation and it's business logic
Aligning Analytics with Business Requirements
Impress interviewers by showing practical knowledge of credit risk model development
Learn the most in demand skill

Who Should Attend

Beginners/students
Experienced analytics professional
working professionals who want to shift towards risk modeling
Data science/machine learning enthusiasts who want to learn a new skill

Target Audiences

Beginners/students
Experienced analytics professional
working professionals who want to shift towards risk modeling
Data science/machine learning enthusiasts who want to learn a new skill

Every time an institution extends a loan, it faces credit risk. It is the risk of economic loss that every financial institution faces when an obligor does not fulfill the terms and conditions of his contracts. Measuring and managing the credit risk and developing, implementing strategies to help lowering the risk of defaults by borrowers becomes the core of any risk management activities.

Financial institutions make use of vast amounts of data on borrowers and loans and apply these predictive and statistical models to aid banks in quantifying, aggregating and managing credit risk across geographies and product lines.

In this course, our objective is to learn how to build these credit risk models step by step from scratch using a real life dataset.

The course comprises of two sections: 1) Developing a credit risk scorecard and 2) Developing a Probability of Default (PD) model. 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 also the primary parameter used in calculating credit risk as per the internal ratings-based approach (under Basel guidelines) used by banks.

In this course, 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.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Chapter 2: Credit Risk Scorecard Development

Lecture 1: Dataset Variables

Lecture 2: Data for Building Model

Lecture 3: Steps in Model Building

Lecture 4: Data Preprocessing 1

Lecture 5: Data Preprocessing 2

Lecture 6: Logistic Regression Model 1

Lecture 7: Logistic Regression Model 2

Lecture 8: Fitting the Model

Lecture 9: Model Performance 1

Lecture 10: Model Performance 2

Lecture 11: Model Performance 3

Lecture 12: Model Performance 4

Lecture 13: Model Performance 5

Chapter 3: Probability of Default Model

Lecture 1: Expected Loss

Lecture 2: Data Exploration 1

Lecture 3: Data Exploration 2

Lecture 4: Creating Default and No Default categories

Lecture 5: Default Rate Calculation

Lecture 6: Default Rate for each Loan Grade

Lecture 7: R Packages

Lecture 8: Training and Test Datasets

Lecture 9: Discarding Attributes

Lecture 10: Converting Data Types

Lecture 11: Attributes with Zero Variance

Lecture 12: Default By States

Lecture 13: Finding Correlation

Lecture 14: Variable Transformation

Lecture 15: Model Development

Instructors

Subhashish Ray
B.E (E&E), MBA (Finance), PRM, SAS and Tableau Certified

Rating Distribution

1 stars: 4 votes
2 stars: 2 votes
3 stars: 8 votes
4 stars: 11 votes
5 stars: 27 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

Menu