Credit Risk Modeling Using Sas

Learn Credit Risk Scorecard Development step by step from scratch. Learn model development, validation & calibration.

What you'll learn

Learn model development and validation

Understand SAS programming steps

Understand SAS programming output interpretation

Learn the process flow in model development, validation and calibration step by step from scratch

Understand the science and logic behind model development

Learn data preparation in depth

Requirements

Basic Knowledge of SAS

Zeal and enthusiasm for learning a new skill

Computer with internet connection

SAS Studio

Description

Credit Risk Modeling is a technique used by lenders to determine the level of credit risk associated with extending credit to a borrower. In other words, it’s a tool to understand the credit risk of a borrower. This is especially important because this credit risk profile keeps changing with time and circumstances. Credit risk modeling is the process of using statistical techniques and machine learning to assess this risk. The models use past data and various other factors to predict the probability of default and inform credit decisions.This course teaches you how banks use statistical modeling in SAS to prepare credit risk scorecard which will assist them to predict the likelihood of default of a customer. We will deep dive into the entire model building process which includes data preparation, scorecard development and checking for a robust model, model validation and checking for the accuracy of the model step by step from scratch. This course covers the following in detail with output interpretation, best practices and SAS Codes explanations :1)  Understanding the dataset and the key variables used for scorecard building2) Development sample exclusions3) Observation and Performance window4) Model Design Parameters5) Vintage and Roll Rate Analysis6) Data Preparation which includes missing values and outlier identification and treatment7) Bifurcating Training and Test datasets 8) Understanding the dataset in terms of key variables and data structure9) Fine and Coarse classing10) Information value and WOE11) Multicollinearity 12) Logistic Regression Model development with statistical interpretation13) Concordance, Discordance, Somer's D and C Statistics14) Rank Ordering, KS Statistics and Gini Coefficient15) Checking for Clustering16) Goodness of fit test17) Model Validation and18) Brier Score for model accuracy

Overview

Section 1: Introduction

Lecture 1 Introduction

Section 2: Conceptual Framework

Lecture 2 Model Design Parameters

Lecture 3 Exclusions

Lecture 4 Exploring Dataset Variables

Lecture 5 Factors of Model Design Parameters

Lecture 6 Business Understanding and Model Selection

Lecture 7 Sample Data Fields

Lecture 8 Vintage Analysis

Lecture 9 Roll Rate Analysis

Section 3: Model Development

Lecture 10 Algorithm for Scorecard Development

Lecture 11 Detecting Missing and Outlier Values

Lecture 12 Removing Missing Values

Lecture 13 Importance of Information Value

Lecture 14 Understanding Fine and Coarse Classing

Lecture 15 Example of Fine and Coarse Classing

Lecture 16 Information Value Range

Lecture 17 Calculating WOE and IV Values

Lecture 18 Creating WOE Variables

Lecture 19 Checking for Multicollinearity

Lecture 20 Concordance and Discordance

Lecture 21 Somers' D and C Statistics

Lecture 22 Rank Ordering, KS Statistics and Gini Coefficient

Lecture 23 Goodness of Fit Test

Lecture 24 Clustering Check

Section 4: Validation and Accuracy Check

Lecture 25 Model Validation

Lecture 26 Brier Score

Students,Risk Analytics Professionals,Statisticians,Experienced Risk Modelers,For Someone who wish to start/shift their career towards risk modeling