Developing Credit Risk Scorecard Using Sas

Credit Risk Scorecard Development and Validation

What you'll learn

Learn Model Development

Understand step by step application of SAS codes

Understand output interpretation

Aligning Analytics with Business Requirements

Complete end to end credit risk scorecard development

Model Validation and Calibration

Requirements

Basic Knowledge of SAS Programming

Basic Knowledge of Statistics

Zeal and enthusiasm to learn new concepts

Description

Credit Risk Analytics is undoubtedly a very crucial activity in the field of financial risk management in banking and finance industries worldwide. This course is meant to teach you the process of creating a credit risk scorecard step by step from scratch and how to validate and calibrate the final model. It takes you through the various steps and the logic behind each and every step with a clear demonstration and interpretation of output using SAS. The course is divided into sections like theoretical framework where the essential theoretical background is explained. In the model development phase the various steps of model building is explained using SAS which includes data preparation, model building with the algorithm, essential checks to perform around the model and addressing vital model parameters like multicollinearity and variable selection. It finally ends with the demonstration of validation steps and calibration of model using SAS. The dataset is also provided for your practice. The dataset used in this course depicts a real world banking dataset and the variables are selected keeping in mind the real world banking practices.This course is suitable for beginners as well as advanced learners or working professionals in this field as all the concepts are explained in a very easy to understand manner.

Overview

Section 1: Introduction

Lecture 1 Introduction

Section 2: Theoretical Framework

Lecture 2 Observation and Performance Window

Lecture 3 Vintage Analysis

Lecture 4 Scorecard Objective

Lecture 5 Let's explore the dataset

Section 3: Model Development

Lecture 6 Logistic Regression Algorithm

Lecture 7 Data Preparation

Lecture 8 Information Value and WOE

Lecture 9 Creating WOE variables

Lecture 10 Multicollinearity

Lecture 11 Hosmer Lemeshow Test

Lecture 12 Concordant and Somer's D

Lecture 13 Rank Ordering, KS and Gini coefficient

Lecture 14 Clustering check

Section 4: Model Validation

Lecture 15 Validation

Lecture 16 Brier Score

Students,Analytics Professionals,Beginners SAS developers intending to transition towards risk analytics