Developing an Application Scorecard using SAS

Developing an Application Scorecard using SAS, available at $19.99, has an average rating of 5, with 32 lectures, based on 5 reviews, and has 23 subscribers.

You will learn about Introduction to Credit Risk and Application Scorecards SAS Fundamentals for Credit Risk Modeling Data Preprocessing for Application Scorecard Feature Engineering for Application Scorecards Building Predictive Models with SAS Model Validation Reject Inference in SAS Enterprise Miner This course is ideal for individuals who are Credit Risk Analysts and Credit Risk Managers or Data Analysts and Data Scientists or Risk Managers and Risk Analysts or Students and Academicians or Professionals Transitioning into Credit Risk It is particularly useful for Credit Risk Analysts and Credit Risk Managers or Data Analysts and Data Scientists or Risk Managers and Risk Analysts or Students and Academicians or Professionals Transitioning into Credit Risk.

Enroll now: Developing an Application Scorecard using SAS

Summary

Title: Developing an Application Scorecard using SAS

Price: $19.99

Average Rating: 5

Number of Lectures: 32

Number of Published Lectures: 32

Number of Curriculum Items: 32

Number of Published Curriculum Objects: 32

Original Price: ₹4,699

Quality Status: approved

Status: Live

What You Will Learn

Introduction to Credit Risk and Application Scorecards
SAS Fundamentals for Credit Risk Modeling
Data Preprocessing for Application Scorecard
Feature Engineering for Application Scorecards
Building Predictive Models with SAS
Model Validation
Reject Inference in SAS Enterprise Miner

Who Should Attend

Credit Risk Analysts and Credit Risk Managers
Data Analysts and Data Scientists
Risk Managers and Risk Analysts
Students and Academicians
Professionals Transitioning into Credit Risk

Target Audiences

Credit Risk Analysts and Credit Risk Managers
Data Analysts and Data Scientists
Risk Managers and Risk Analysts
Students and Academicians
Professionals Transitioning into Credit Risk

The “Developing an Application Scorecard using SAS” course is designed to equip participants with the knowledge and practical skills necessary to build robust application scorecards using SAS software. Application scorecards are critical tools used by financial institutions to assess the creditworthiness of new applicants and make well-informed lending decisions. This course will guide participants through the entire process of developing an application scorecard, from data preparation and variable selection to model development, validation, and deployment.

Course Objectives:

By the end of this course, participants will:

Understand the fundamentals of credit risk assessment and the significance of application scorecards in the lending process.
Learn how to use SAS software for data manipulation, data preparation, and statistical analysis in the context of credit risk modeling.
Develop expertise in data preprocessing techniques, including data cleaning, imputation, and outlier handling.
Master the process of feature engineering to create informative variables for the application scorecard.
Gain practical experience in building and optimizing predictive models using SAS for credit risk evaluation.
Implement model validation techniques to ensure the accuracy and reliability of the application scorecard.
Understand the best practices for scorecard implementation, monitoring, and ongoing maintenance.

Target Audience: This course is ideal for credit risk analysts, risk managers, data analysts, and professionals working in financial institutions who want to enhance their skills in application scorecard development using SAS. It is also suitable for individuals interested in credit risk modeling and decision-making in the lending industry.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Chapter 2: Conceptual Framework

Lecture 1: Model Design Parameters

Lecture 2: Exclusions

Lecture 3: Exploring Dataset Variables

Lecture 4: Factors of Model Design Parameters

Lecture 5: Model Selection

Lecture 6: Vintage Analysis

Lecture 7: Roll Rate Analysis

Lecture 8: Application Scorecard Development Factors

Chapter 3: Model Development

Lecture 1: Algorithm for Scorecard Development

Lecture 2: Exploring the Dataset

Lecture 3: Data Preparation 1

Lecture 4: Data Preparation 2

Lecture 5: Data Preparation 3

Lecture 6: Importance of Information Value

Lecture 7: Understanding Fine and Coarse Classing

Lecture 8: Example of Fine and Coarse Classing

Lecture 9: Information Value Range

Lecture 10: Performing Fine and Coarse Classing

Lecture 11: Binning- Coarse Classing

Lecture 12: Creating WOE Variables

Lecture 13: Checking for Multicollinearity

Lecture 14: Logistic Regression Model

Lecture 15: Model Performance Checks

Lecture 16: Rank Ordering, KS Statistics and Gini Coefficient

Chapter 4: Model Validation

Lecture 1: Model Validation

Lecture 2: Brier Score

Lecture 3: Model Validation- Practical

Chapter 5: Reject Inference

Lecture 1: Reject Inference

Lecture 2: Handling Reject Inference 1

Lecture 3: Handling Reject Inference 2

Lecture 4: Withdrawal Inference

Instructors

Subhashish Ray
B.E (E&E), MBA (Finance), PRM, SAS and Tableau Certified

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5 stars: 5 votes

Frequently Asked Questions

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You can view and review the lecture materials indefinitely, like an on-demand channel.

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