We have worked with a number of financial services firms over the years on risk and marketing projects, and are very familiar with the interaction between these competing dynamics. The interesting thing about these influences (and the challenge in dealing with them) is that they are inversely correlated. So, individuals with high credit risk tend to respond well to marketing offers, while those with low credit risk respond poorly to solicitations.
How do you deal with this dilemma? We have used several techniques over the years that have proven to be effective:
1). Develop a custom risk model. In addition to have the benefit of being more powerful than a ‘generic’ risk score (such as FICO), a custom model will carve out a slightly different universe of credit worthy people. It is those individuals that your custom model deems to be creditworthy, while scoring on the margin with FICO, that will respond to marketing solicitations. The FICO score is an excellent indicator of saturation.
2). Develop custom response models only on the relevant credit universe. If your product is aimed at 640-720 FICO, make sure that only individuals in that range are included in a modeling initiative.
3). Segment by risk. In addition to developing on the relevant credit universe, you should consider using FICO as a segmentation tool. For example, you may want to build one response model for individuals scoring 640-680 and another for individuals scoring 680-720. Why? The drivers of response can be quite different by credit strata.
I would not include the FICO score in a response model. Due to correlation issues, doing this would often remove the benefits of segmenting by FICO.
In summary, use the FICO score (which is a highly accurate gauge of the type of credit offers being sent to each individual) as a tool to segment the universe. This will remove the ‘credit bias’ that will otherwise dominate a response model. Additionally, utilize a custom risk score to assess the true credit risk of your market.