Developing Credit Scorecards using SAS Visual Data Mining and Machine Learning
May 14 • 2 p.m. BST • Cost: Complimentary
About the webinar
With the adoption of new technologies, organisations that offer credit to new or existing customers are trying to find faster and more accurate approaches to measure creditworthiness.
In this webinar, we’ll run through an example of credit scoring model analysis on financial data using the risk modelling add-on for SAS Visual Data Mining and Machine Learning (VDMML). We’ll develop a credit scorecard that provides statistical odds or probability of the level of risk associated with applicants or customers. By using a credit scorecard to inform our decision-making process, we are more likely to identify high risk applicants or customers and reduce the risk to the overall portfolio. It will also help to support rapid growth with less risk and higher profitability.
You will learn how to:
- group the characteristic variables of your data into attributes.
- use a logistic regression model to create an initial scorecard.
- perform reject inference on the initial model.
- create the final scorecard using the information that is obtained in the previous steps.