"Treat me fairly."

What is Fair? fair 'fa(e)r, 'fe(e)r adj: marked by impartiality and honesty: free from self-interest, prejudice, or favoritism (Webster's Collegiate Dictionary).

Objective. Reliable. Impartial.

The very words associated with scoring imply that it gives people a fair shake. And it's not just scoring: All of Fair, Isaac's products and services help lenders and other businesses remove guesswork, inconsistency and misconceptions from their operations. As a result, each prospect, each applicant, each consumer - each person - receives an unbiased appraisal based only on relevant information.

Discrimination Doesn't Add Up

It's an ugly fact that as recently as the early 1970s some lenders in the U.S. blatantly gave preference to white applicants. In developing scorecards, Fair, Isaac representatives had to explain that, apart from the ethical considerations, this just didn't make good business sense. For instance, about 25 years ago, one customer asked us to include race as a factor in its custom scoring system. At the time, that lender marked relevant applications with an N or an S, for Negro or Spanish. Fair, Isaac compared the performance of these borrowers with that of white borrowers and showed the company that traditional scoring factors - such as previous performance on loans - worked best in assessing risk. Race just wasn't predictive; as a result, the scorecards built for the company did not include race. A subsequent Justice Department investigation ended with the customer being ordered to use scores as the primary guide in accepting or declining applicants.

The Equal Credit Opportunity Act, adopted in the mid-'70s, helped undo many prejudicial practices, and sometimes relied on scoring as its "enforcer." Several lenders entered into consent decrees requiring that they use scoring in place of judgmental decision making, to remove the possibility of race- or gender-based discrimination.

Another Look at Low-Income Applicants

A credit applicant's risk score doesn't meet the lender's criteria. End of story? Not necessarily. Fair, Isaac has pioneered a new kind of scorecard focused on applicants with lower incomes, who may have slightly different payment patterns than the general population. The Low-Moderate Income (LMI) scorecard doesn't lower the lender's risk criteria; rather, it helps lenders find more people who meet those criteria.

The LMI scorecard can help a lender make a marginal improvement to its approval rate for lower-income applicants. It's a margin lenders covet. And for lower-income applicants who might find credit a bit harder to obtain in the first place, being approved is 100 percent better than the alternative.

Since 1995, scoring has made its biggest strides into the world of mortgage lending. In that year mortgage investors such as Freddie Mac and Fannie Mae endorsed Fair, Isaac credit bureau risk scores as part of the underwriting process because the scores zero in on likely payment performance alone, ignoring subjective considerations.

"Tools that, in an unbiased manner, help separate loans that are likely to perform well from loans that are less likely to perform well ensure the continued availability of mortgage money to all creditworthy borrowers," said Freddie Mac's July 1995 industry letter on "The Predictive Power of Selected Credit Scores." "Credit scores are an effective way to help [mortgage lenders] promote this goal. "Fannie Mae echoed this statement in an October 1995 letter to lenders: "Credit scores can also be used effectively in efforts to expand home-ownership opportunities to underserved households."

The benefits of scoring for mortgage borrowers mirror those in other industries: faster service, less paperwork, and often a greater chance of being approved.

Price counts in choosing insurance, which leaves insurers with a dilemma. Setting one premium for everybody means that the "good" risks are effectively paying for the "bad" risks. If the company takes a hit from a few large losses, prices rise even for customers who will probably never file a claim.

Since scores can accurately forecast losses for different groups of policyholders, some insurers are using score to help determine price. A low-risk customer may pay much less than the standard premium, while a high-risk customer will pay a higher premium.

Without accurate pricing, the 'bad' risks are almost always subsidized by the 'good' risks. When an insurance company can set its premiums according to the individual risk, it benefits not only itself but its clients.