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   ARTICLE   |   From Scotsman Guide Residential Edition   |   June 2014
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Does Your Underwriting Face a Systemic Risk?

Giving too much weight to FICO scores may pose major risks for lenders


Some mortgage-industry analysts have argued that there’s an inherent flaw in lenders focusing on a single risk score when making their underwriting decisions. Of course, these arguments often focus on problems that could arise from a borrower’s FICO score being the  primary tool on which lenders rely. When the industry relies on a single point of  reference to judge a consumer’s creditworthiness, lenders may expose themselves to systemic risk. Should this single reference point prove inefficient or inaccurate, the entire underwriting system could crumble.

If you take this line of reasoning a step further, the systemic risk arguably doesn’t lie in an analytic solution like a FICO score, but instead lies in the use of a single point of reference. Scores such as FICO are based on credit-bureau data and use methodology that’s consistent with those of other credit-bureau scores in the marketplace. These scores all share a common data source. Consequently, the systemic risk that lenders face is based on their use of a single data source; if the underlying credit-bureau data fails, then all credit- bureau scores fail.

For example, some lenders may choose to suppress reporting credit- limit information on a credit report for competitive reasons. The thinking is that if credit card issuer No. 1 discloses a $10,000 limit on a credit report, credit card issuer No. 2 may use that information to woo a consumer away with an offer for a $12,000 limit.  To avoid this situation, credit card issuer No. 1 could suppress reporting for its credit limit.

In the calculation of scores, however, credit limit is a key component in the computation of credit utilization, which is a key component of credit-bureau scores  (visit to learn more about this subject). The strategic decision to suppress credit limits could adversely impact credit-bureau scores overnight, and in this case, the effectiveness of any score by any vendor based on this data would be compromised.

It therefore makes sense to broaden the data used in your credit decisions. By  diversifying the information in a decision, the impact and efficacy of any particular score becomes less impactful to that decision.

There’s also a fair-lending implication to this argument. By judging credit applicants by the same tools across many lenders, a consumer could be consistently turned away from credit and could be essentially shut out of the entire credit system. Given the increased scrutiny of fair-lending practices, it behooves lenders to broaden their scope beyond this shared risk and abandon the cookie-cutter approach of requiring a FICO score that exceeds a certain cutoff. If borrowers are qualified and disqualified by the same score, lenders will likely be unable to make refined decisions when it comes to giving loans. A borrower who is rejected by one lender is likely to be rejected by all lenders because the underlying credit data doesn’t change — in fact, that underlying credit data indicates increasingly greater risk as inquiry after inquiry piles up. The borrower’s only choices would be to seek out more expensive credit or opt out of the credit system entirely.

Even more problematic is that overreliance on credit-bureau data means that those without a credit footprint are essentially credit invisible. That is, these potential borrowers are often unable to establish credit because no mainstream lender is willing to underwrite somebody who can’t be assessed by credit-bureau data alone. These credit-invisible consumers skew toward underserved minorities such as black and Hispanic homebuyers.

The solution to these risks is for lenders to invite other data sources into the decisionmaking process. For example, alternative data such as public records, property ownership records and wealth may provide additional information about a person’s creditworthiness. This data also adds depth to decisions based on credit- bureau data by indicating high- and low-risk  behaviors outside of a consumer’s  wallet. Lenders could then make more  refined decisions based on the combination of broader data elements in which credit bureau data is a piece of the decision rather than the sole driver of the decision.

Alternative data also may serve as a  replacement to credit-bureau data when such data is unavailable. It may also help certain consumers — especially underserved  minorities — emerge from their credit- invisible status. Many of these consumers could then open affordable credit accounts, purchase cars and homes, or use a credit card with reasonable terms.

In short, alternative data deserves to be a key part of mortgage lenders’ decision-making strategies, as this data may provide increased predictive value when used with a credit-bureau score, and enables scoring of many consumers who can’t be scored using credit-bureau data alone. This in turn may help reduce the systemic risk of underwriting campaigns across the industry.  


Jeffrey Feinstein is senior director of analytic strategy at LexisNexis, where he’s responsible for developing the LexisNexis RiskView Credit Risk Score. He leads innovation efforts to bring new analytic solutions to the market leveraging LexisNexis data. Before joining LexisNexis in 2010, Feinstein developed expertise in credit data and received three U.S. Patents for his innovations in credit modeling. He holds a PhD from Ohio State University in experimental psychology and applied statistics. E-mail


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