By: Wendy Greenawalt
Today, most lenders evaluate tri-bureau credit data when making lending decisions. Credit attributes are the building blocks for creating models, scorecards, segmentation and policy rules. Why is creating tri-bureau attributes so difficult? The main challenges are assessing the bureau data that is available, deriving meaningful information from that data and then equalizing or minimizing the differences inherent to the data available from the credit bureaus.
While this process may seem straight forward, defining an industry designation or a series of attributes within that industry can take months of analysis and careful consideration of trade-offs. Missing even one data element can have a major impact to lending decisions and the portfolio mix of an organization.
For example, let’s look at a very basic attribute like total number of trades. When creating this attribute, an organization has to decide what constitutes a trade. For instance, is a collection account a trade that should be included in the count? Again, this may seem trivial, but could have a significant impact to the risk associated with a consumer when combined with other credit data.
Whether credit attributes are created and managed internally or purchased from an attribute provider, the process of defining and leveling credit bureau data across bureaus requires significant time and resources. Therefore, ensuring the attributes used are statistically accurate and predictive is vital to the long-term success of an organization.