Credit Risk Modeling using SAS

Credit Risk Modelingis 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 building

2) Development sample exclusions

3) Observation and Performance window

4) Model Design Parameters

5) Vintage and Roll Rate Analysis

6) Data Preparation which includes missing values and outlier identification and treatment

7) Bifurcating Training and Test datasets

8) Understanding the dataset in terms of key variables and data structure

9) Fine and Coarse classing

10) Information value and WOE

11) Multicollinearity

12) Logistic Regression Model development with statistical interpretation

13) Concordance, Discordance, Somer’s D and C Statistics

14) Rank Ordering, KS Statistics and Gini Coefficient

15) Checking for Clustering

16) Goodness of fit test

17) Model Validation and

18) Brier Score for model accuracy

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• 5 stars: 22 votes

Frequently Asked Questions

How long do I have access to the course materials?

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

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