Improving Scorecard Accuracy

It often happens that scorecard accuracy leaves much to be desired. Moreover, the discrepancy between the expected and actual performance of the scorecard is often noticed at the final stage of it’s implementation, when most of the resources have already been invested.

How can we ensure that everything possible has been done to develop a scorecard of the highest quality, and how can we pinpoint and prevent potential errors?

To answer that question, let us consider the process of developing a scorecard. This process contains the following stages:

1. Sampling
2. Statistical evaluation of borrowers' characteristics
3. Formation of training and validation datasets
4. Use of the regressional procedure for scorecard calculation
5. Evaluation of the scorecard's quality

We cannot significantly impact the results of the regression algorithm performance by adjusting its parameters. Correspondingly, we cannot influence the performance of the scorecard at the stage of evaluating its quality.

Hence, the first three stages dealing with the processing and preparation of the credit portfolio data are the most important.

Let’s look at a few ways we can locate and prevent errors that will have a negative impact on the quality of the scorecard.

1. Sampling