Credit Risk Modeling using SAS

🚀 Master Credit Risk Modeling using SAS 🎓 Course Headline: Learn Credit Risk Scorecard Development step by step from scratch. Dive into model development, validation & calibration!

👀 Understand Credit Risk Instantly

Credit Risk Modeling is a fundamental tool for lenders to assess the risk of loan defaults. It's an ever-evolving field that requires a deep understanding of statistical models and machine learning techniques. In this course, we demystify the process of credit risk modeling using SAS, ensuring you can predict customer default probability and make informed credit decisions.

🔥 What You Will Learn:

Course Highlights:

1. Data Insights: Get to grips with the dataset and identify key variables for your scorecard.
2. Sample Selection: Learn how to exclude observations that could skew your sample.
3. Performance Windows: Understand how to observe and interpret performance over different time frames.
4. Model Parameters: Set up your model design parameters effectively.
5. Vintage & Roll Rate Analysis: Analyze credit performance over the life of a loan portfolio.
6. Data Cleaning: Master the art of handling missing values, outliers, and ensuring data quality.
7. Classification Techniques: Fine-tune your approach with fine and coarse classing methods.
8. WOE Analysis: Discover the power of Weight of Evidence to enhance your model's predictive accuracy.
9. Multicollinearity Check: Avoid overfitting by detecting and managing multicollinearity.
10. Logistic Regression Mastery: Develop logistic regression models with a clear understanding of statistical implications.
11. Evaluation Metrics: Learn to evaluate model performance with concordance, discordance, Somer's D, C statistics, and more.
12. Rank Ordering & KS Statistics: Perfect the ordering of observations and test for deviations from true probabilities.
13. Gini Coefficient & Model Fit Test: Calculate the Gini coefficient to assess model performance and conduct goodness-of-fit tests.
14. Cluster Analysis: Check for and manage clustering in your data.
15. Model Validation: Ensure your model's robustness across different datasets and scenarios.
16. Brier Score Calculation: Determine the accuracy of your model with this key performance indicator.

🔍 Dive Deep into Modeling

This course is structured to take you from the basics of understanding credit risk to developing, validating, and calibrating a robust credit risk scorecard using SAS. Each step of the model development process is covered in detail, with a focus on output interpretation, best practices, and clear explanations of the SAS code used.

• Real-World Data Analysis: Learn to work with real datasets, understand their structure, and identify key variables.
• Statistical Techniques: Gain insights into statistical methods like logistic regression and their role in credit scoring.
• Model Evaluation: Master the art of evaluating your model's performance using various statistics and metrics.

🎯 Who Is This Course For?

• Aspiring and existing data scientists interested in credit risk modeling.
• Credit analysts looking to enhance their analytical skills.
• Financial professionals aiming to understand the underlying principles of credit scoring.
• Anyone interested in learning advanced statistical techniques with SAS for financial applications.

📅 Take the Next Step

Join us on this journey to become a Credit Risk Modeling expert using SAS. Enroll now and transform your career! 🚀

Note: This course assumes no prior knowledge of SAS or credit risk modeling. By the end of this course, you will be equipped with the skills to confidently tackle credit risk challenges in the financial industry.