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
