Basel IRB Credit Risk Modeling using SAS PD, LGD, EAD

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Free Download Basel IRB Credit Risk Modeling using SAS PD, LGD, EAD
Published 12/2025
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 7h 3m | Size: 3.87 GB
A Hands-On Guide to build Basel regulatory credit risk models under Basel IRB using SAS

What you'll learn
Gain a comprehensive understanding of the Basel Advanced Internal Ratings-Based (A-IRB) approach and its role in regulatory capital requirements
Learn how to develop and implement Probability of Default (PD), Loss Given Default (LGD), and Exposure at Default (EAD) using SAS
Master techniques for validating credit risk models in line with regulatory expectations and best practices
Use SAS to apply statistical methods, such as logistic regression and scorecard development, to enhance credit risk modeling
Gain practical experience in building and analyzing credit risk models using SAS
Develop the ability to prepare and manage large datasets for credit risk modeling in SAS
Requirements
Interest in Basel Regulations
Basic knowledge of SAS or any other programming language will make it easier to follow the practical exercises
Basic Understanding of Credit Risk
Basic Knowledge of Banking or Finance
Access to SAS Studio or SAS OnDemand for Academics (a free, cloud-based version of SAS) is required to practice the exercises and run the provided scripts
A stable internet connection and a modern web browser are needed for accessing SAS OnDemand and course materials
Description
Welcome to "Basel IRB Credit Risk Modeling using SAS: PD, LGD,EAD"-a hands-on course designed to teach you how to build and implement Basel-compliant credit risk models using SAS. Whether you're a finance professional, data scientist, or student, this course will give you the tools and knowledge to effectively model credit risk metrics like Probability of Default (PD), Loss Given Default (LGD), and Exposure at Default (EAD) within the Basel IRB framework.What You'll Learn:Credit Risk Modeling Basics: Gain a strong understanding of key credit risk metrics, including PD, LGD, and EAD, and their role in the Basel IRB framework.Basel IRB Compliance: Learn how Basel regulations shape the development and validation of credit risk models for regulatory compliance.SAS for Credit Risk Modeling: Master the use of SAS programming to build and implement PD, LGD, and EAD models, including data manipulation and visualization.Real-World Case Studies: Apply your learning to real-world banking scenarios and datasets, making the course relevant to actual financial industry practices.Advanced Techniques: Learn advanced modeling concepts such as model calibration, backtesting, stress testing, and portfolio-level risk aggregation.Why Take This Course?Hands-On Learning: Engage with practical examples and build Basel IRB models using SAS step-by-step.Industry-Relevant Skills: Acquire the knowledge needed to work on real credit risk projects in banking and finance.Comprehensive Coverage: From foundational concepts to advanced techniques, this course covers all aspects of Basel IRB credit risk modeling.Beginner-Friendly: Whether you're new to SAS or credit risk, this course starts with the basics and builds to more advanced topics.Who Should Enroll?Aspiring credit risk analysts or data scientists.Banking professionals aiming to understand Basel IRB compliance.SAS programmers looking to specialize in financial risk modeling.Students and professionals interested in financial regulations and risk management.By the end of this course, you'll not only understand Basel IRB credit risk models but also have the skills to implement them in SAS for practical, real-world use. Join today and enhance your expertise in credit risk modeling!
Who this course is for
Risk Management Professionals
Data Analysts and SAS Users
Banking and Finance Professionals
Students and Career Changers
Academics and Researchers
IT and Data Science Professionals
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Credit Risk Modeling Using Sas
Last updated 5/2023
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.65 GB | Duration: 3h 50m​

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

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