@Nanjundan Chinnasamy To incorporate detailed explanations at the predictor level and ensure they are accessible during runtime for visual representation you can follow these steps: 1. Create a Scorecard Rule: Start by creating a scorecard rule to define the predictors and their values. This can be done by navigating to the Scorecards landing page in Pega Customer Decision Hub and creating a new scorecard. Ensure you define the predictor values and map score ranges to decision results. 2. Testing the Scorecard Logic: You can test the scorecard logic from the Scorecard rule form to get detailed insights into how scores are calculated. The test results will show the score explanations for all the predictors used in the calculation which can help you validate and refine the scorecard design. 3. Enable Runtime Explanations: To enable detailed explanations at runtime you need to configure the scorecard to output these details. This involves setting up the scorecard to include execution details and ensuring these are accessible during runtime. This can be done by configuring the scorecard result to include detailed explanations for each predictor. 4. Visual Representation: For visual representation you can use Pega's reporting and visualization tools to display the scorecard results including the detailed explanations for each predictor. This can be done by creating reports or dashboards that pull in the scorecard data and display it in a user friendly format. By following these steps you can ensure that detailed explanations at the predictor level are accessible during runtime and can be visually represented for better decision making.

⚠ This is a GenAI-powered tool. All generated answers require validation against the provided references.

Scorecard rules 

Building a scorecard to calculate the credit score