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Improving Scorecard Accuracy1. SamplingThis stage starts by downloading borrower-related data from a data warehouse. At this stage it is important to preserve the main requirements for the working sample: its representativeness and randomness. Representativeness means the maximum accuracy of the borrower's characteristics contained in the sample in relation to the actual borrower's characteristics in the credit portfolio. This requirement is absolutely natural, since the scorecard reflects the specifics of the dataset used for its development. Randomness means that loan application data should be included into the working sample independently.
If the requirements for representativeness and randomness are not fulfilled, that will unavoidably impact the performance of the scorecard.
In practice, the working sample is formed by randomly selecting credit cases corresponding to the select timeframe, from the data warehouse. In this situation, the timeframe selected for the working sample is a decisive factor. To better understand this fact, let's consider the mechanism of forecasting based on the time factor:
Performance Period — the period of time during which we collect information on the credit quality of borrowers. It ends with the observation point that corresponds to the moment of the forecast. Time horizon – the borrower's credit quality is determined at the end point (outcome point) of the time horizon. For example, if the observation period is 12 months and the time horizon is 6 months, based on the analysis of the borrower's behavior during the 12 months we can predict his/her state in 6 months. Here we need to pay attention the following factors: the observation period must be uninterrupted and historical data must be close to the actual characteristics of the credit portfolio.
If the observation period is interrupted, that will influence the performance of the scorecard. To achieve acceptable results of forecasting at the stage of sampling, we must use data for the last three (or more often two or one) years.
We can prevent errors during sampling in the following two ways: firstly, by directly controlling the procedures responsible for downloading data from the warehouse, and, secondly, by evaluating the statistical characteristics of the borrower.
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