3 Reasons to use Alternative Credit Data in Prescreen Campaigns

by Rich Heath

Rich Heath

In today’s competitive marketplace, it is important to deliver innovative financial solutions that create opportunities for growth in lending and consumer spending. Many of the largest U.S. lenders who work with ID Analytics have been telling us that prescreen marketing campaigns which rely solely on targeting consumers with prime credit bureau scores are realizing diminishing returns in the face of increased competition. Declining response rates have prompted some lenders to consider new approaches such as using alternative credit data in prescreen campaigns.

A Mercator research brief indicates that 25 percent of the adult U.S. population are “credit invisible,” meaning they fall outside the view of traditional scoring models and one-third of millennials don’t have sufficient data to be scored by the national credit reporting agencies.¹ There are millions of Americans whose credit profiles suffered following the recession of 2009 and are now in credit recovery.² Considering these factors, there is an opportunity expand credit offers to candidates who may warrant consideration. Alternative credit data can help lenders identify more consumers who meet their credit criteria, increase response rates, and reduce campaign costs.

Here are three reasons incorporating alternative data insights into credit decisioning processes can enhance prescreen campaigns:

1. Identify more potentially creditworthy, underserved consumers.

Several U.S. consumer segments who lack a prime bureau score possess similar creditworthiness to those with a prime bureau score.³ Alternative credit data can provide credit risk insight into three segments:

  • National bureau no-scores – consumers who don’t have a traditional credit score
  • Marginal – consumers underestimated by traditional bureau scores
  • Credit risers – consumers whose improving risk profile may not be reflected in traditional credit assessments


Alternative credit data provides insight to many modern credit activities and responsibilities, including wireless, cable and utility payments; payday and subprime lending; and marketplace and online lending. These additional insights can provide an improved understanding of consumer credit risk, which can uncover numerous risk-appropriate consumers currently overlooked in traditional credit assessments and help lenders identify more prospects for prescreen campaigns.⁴

2. Increased Response Rates

See reason number one. If lenders are able to identify an expanded pool of creditworthy consumers, they can increase the number of prescreen offers they send. Consumers overlooked by traditional assessments have lower access to credit and ID Analytics has seen underestimated consumers have a higher propensity to respond to prescreen offers.⁵

3. Reduce campaign costs

See reasons number one and two. By identifying a broader range of targetable consumers, enterprises can extract more value out of existing marketing assets, reducing the need to purchase additional lists. Alternative credit data insights can help optimize offers through predictive risk assessment—allowing enterprises to improve offer determination across the credit spectrum.⁶ Increased response rates and reduced campaign costs can lead to higher ROI of prescreen marketing campaigns.

Arguably, alternative credit data can help change the economics of banking by allowing more consumers access to financial products at a reduced cost.

To learn why many of the largest lenders in the U.S. are incorporating alternative credit data across the customer lifecycle to gain a competitive advantage, download our new brief Leveraging Alternative Data for Prescreen and Portfolio Management.


Rich Heath is a Principal Analytical Consultant at ID Analytics


1.  Mercator Advisory Group, Alternative Data for Credit Decisioning: A Primer. February 2017, p. 3 & 5.

2.  Ibid, p. 5.

3.  ID Analytics, Credit Optics Prescreen Case Study. 2018.

4.  Ibid.

5.  Ibid.

6. ID Analytics, Driving Real Results from Alternative Data. April 2017.