Published 1/2023
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 850.29 MB | Duration: 1h 53m
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
Homepage
Code:
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