The Federal Reserve's decision to permit card issuers to use income estimation models to meet a requirement imposed by the Credit Card Accountability, Responsibility and Disclosure (CARD) Act to assess a customer's ability to repay a loan makes good sense.

Income estimation models offer many advantages and uses to the financial services industry. A truly effective estimation model can capture a complete financial picture of the borrower, providing greater insight into the ability to meet obligations.

Such models will play an important role in resolving the financial crisis, which was characterized by an unprecedented rise in household debt. Income estimation models even let lenders accurately segment defaulted borrowers and maximize collections.

These models are objective third-party estimates that avoid bias and are consistent and compatible with issuers' current processes for running credit reports. By validating a borrower's income, lenders no longer need rely strictly on consumer-supplied information, an obviously flawed process that opens the door to fraud. Moreover, by using reliable third-party estimates, lenders can improve efficiency by prioritizing time-consuming and expensive verification to those borrowers most likely to be misstating income, while keeping their business practices in alignment with recent regulatory mandates.

The models also play a role in protecting consumer privacy. In response to the initial rules proposed by the Federal Reserve, which would have required income information to be obtained directly from consumers, many retail card issuers expressed concern that such a requirement risked privacy breaches, especially in a retail store or at a teller window. They were concerned that eavesdroppers could overhear or potentially see borrowers' income information.

Furthermore, refreshing stated income information from loan customers, which would involve reaching out to borrowers to ask for their current income, could create unwelcome opportunities for phishing schemes. But income estimation models protect the privacy of consumers by keeping estimates behind the scenes, as with credit scoring.

A key benefit of income estimation models is that they validate consumer income in real time.

Not all models are created equal, however. When considering an income estimation model, it is important to consider the source of the income data upon which the model was developed. The best models rely on verified income data and cover all income sources, including wages, rent, alimony, investments and Social Security.

It is important for lenders to examine these models with an eye toward compliance with fair-lending rules, including the Fair Credit Reporting Act, or FCRA, and Equal Credit Opportunity Act, or ECOA. Certain demographic predictors used in these models, such as where a person lives, age or race, must remain strictly within ECOA guidelines in order to ensure that they do not foster discrimination. Avoiding potentially discriminatory practices is crucial not only for complying with FCRA and ECOA but also for maintaining consumer trust.

As tighter regulations and higher debt burdens affect lenders, the need for tools that can accurately assess borrowers' ability to pay has never been greater. Income estimation models have emerged to let lenders make accurate decisions efficiently, reduce risk and improve customer treatment, all in compliance with regulations.

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