Prescreening Solutions in a Box
For the past 30 or so years, prescreening customers before starting credit marketing outreach has been the purview of very large financial institutions. We’ve all received direct mail in the past that begins with, “You’re pre-approved” or “You’re pre-qualified,” which means that you meet the lender’s minimum lending criteria. The offers are based upon data held with the credit bureaus, and the lender is legally obligated to make you a firm offer of credit once you’ve met lending criteria. Unfortunately, a traditional prescreen marketing campaign is costly, complex, compliance-driven, and labor-intensive making the credit marketing daunting, particularly for community financial institutions so, these financial institutions either do credit marketing infrequently or not at all. That’s now changing.
The lending markets show some inefficiencies meaning that a material amount of loan debt is mispriced relative to current lending rates. Current estimates put mispriced debt at 15% of the $14.6 trillion (T) in total consumer debt, or $2.2T. That represents a significant refinancing opportunity for community financial institutions. It’s an opportunity to help customers reduce their borrowing costs by refinancing mispriced debt held elsewhere with their own bank or credit union.
Given the opportunity to do well by doing good, community financial institutions need the technology to find and refinance mispriced debt held elsewhere, automatically. Current methodologies are not cost-effective. Here’s why…
Cost and Complexity of Traditional Prescreening
The process of developing a prescreening list today is labor-intensive and complex. First, the lending team must extract and communicate lending criteria to the credit bureau involving multiple documents, emails, and clarifying phone calls that slow the process. Once the lending criteria and any other selection criteria are communicated to the credit bureau, it is translated into query language that the credit bureau’s business analyst can execute on the bureau’s vast database(s).
Next comes an anonymized test file sent from the bureau to the bank which may require correction and a re-run before the prescreen file is run with real names in the output file. This process is costly because many hours of labor are necessary regardless of whether the exercise produces one record or one million records.
When a ‘clean’ list is available, the lender must develop a marketing campaign to promote the offer, adding another layer of cost and complexity to the process.
The bottom line is – computers can do this work better, faster, and cheaper than humans.
Prescreening data and financial services marketing campaigns are regulated under the Fair Credit Reporting Act (FCRA) and Unfair, Deceptive, or Abusive Acts or Practices (UDAAP). Therefore, every word and numerical value in the offer must be scrutinized for legal compliance. The best solution is to use a fully compliant template into which personalized values flow.
We are at a point in history where fully personalized and always relevant credit marketing can be largely automated. An automated process slashes cost, complexity, and compliance risk to a level where nearly any financial institution can leverage this powerful tool regardless of size. The goal remains to deepen relationships and develop trust with existing creditworthy customers, the method of getting there is prescreen-in-a-box.