Credit Risk Modeling Using R Programming

Learn end to end credit risk scorecard and probability of default (PD) modeling using R Programming with real-life data

What you'll 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

Requirements

A computer (Windows/Mac/Linux). That's it!

No prior experience is required. This course is meant for beginners and the explanation is from scratch

Zeal and enthusiasm to learn a new skill

Description

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.   

Overview

Section 1: Introduction

Lecture 1 Introduction

Section 2: Credit Risk Scorecard Development

Lecture 2 Dataset Variables

Lecture 3 Data for Building Model

Lecture 4 Steps in Model Building

Lecture 5 Data Preprocessing 1

Lecture 6 Data Preprocessing 2

Lecture 7 Logistic Regression Model 1

Lecture 8 Logistic Regression Model 2

Lecture 9 Fitting the Model

Lecture 10 Model Performance 1

Lecture 11 Model Performance 2

Lecture 12 Model Performance 3

Lecture 13 Model Performance 4

Lecture 14 Model Performance 5

Section 3: Probability of Default Model

Lecture 15 Expected Loss

Lecture 16 Data Exploration 1

Lecture 17 Data Exploration 2

Lecture 18 Creating Default and No Default categories

Lecture 19 Default Rate Calculation

Lecture 20 Default Rate for each Loan Grade

Lecture 21 R Packages

Lecture 22 Training and Test Datasets

Lecture 23 Discarding Attributes

Lecture 24 Converting Data Types

Lecture 25 Attributes with Zero Variance

Lecture 26 Default By States

Lecture 27 Finding Correlation

Lecture 28 Variable Transformation

Lecture 29 Model Development

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