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Sub-Prime Lending Mess Not Technology’s Fault

Experts say technology and good decision-making go together in avoiding loan risk.

It has been front-page news for months -- the mortgage meltdown is threatening the U.S. economy. As lenders of all sizes face scrutiny over risky lending practices, questions remain about how technology could have been used to soften or prevent the sub-prime lending mess.

Some experts say the crisis was inevitable. Ted Landis, a Phoenix-based senior executive in Accenture's financial services group, says the signs of a looming meltdown in the market have been evident since the introduction of sub-prime lending a decade ago. "It has been a developing story since the late '90s," he contends.

According to Landis, lenders reached a point over the past two or three years at which they needed to create demand to justify the investments they made earlier this decade to scale up loan operations. "Lenders needed to continue to create demand to meet the scale they had built," he asserts. "To do that they loosened credit requirements and started to create exotic credit products to entice consumers. It was real demand, but artificial in that it created a potential for time-delayed problems once teaser rates expired."

However, technology was by no means the root cause of the current situation, says Lowell Alcorn, a managing director with McLean Va.-based BearingPoint. According to Alcorn, the technology works just fine -- it's the degree of risk lenders programmed into their lending models that led to the current market problems. "The technology absolutely works," he says. "But when you choose to take aggressive underwriting standards, you set yourself up. It's all in how aggressive a lender wants to be. The technology will only do what you tell it to do."

While Accenture's Landis agrees that the technology itself was not at fault, "It enabled people to create and accept decisioning criteria that were not fully vetted or validated for risk exposure," he adds. "Although you can always develop more-robust, inclusive models and apply more criteria to the decisioning process, it was the acceptance of inaccurate and flawed data into the decision process that was a problem."

Solutions Rise From the Ashes

As with any crisis, a slew of technologies are likely to crop up to help financial services companies avoid future hardship, comments BearingPoint's Alcorn. "In the past when this happened, new technology emerged," he comments. "I think business process management is going to help the automation process further and kick some of the cost out of the systems. Data analytics will be improved to encompass factors in addition to credit scores and property values to have a better understanding of risk."

Tom Quinn, systems integration VP for scoring with Fair Isaac (Minneapolis), maintains that the FICO credit-scoring model is still one of the best defenses against risky credit decisions. "Specific to credit-scoring technology, our research finds that FICO scores continue to be highly effective in rank ordering consumer mortgage risk," he asserts.

And even that tried-and-true credit-scoring method continues to evolve -- Fair Isaac recently developed the next version of FICO. According to Quinn, "Research results show a strong predictive lift of about 10 to 15 percent for nonprime mortgage loans [with the new FICO method]. We are also working on forward-looking analytic solutions that a mortgage lender could use to help determine how much incremental debt the applicant can safely take on."

Accenture's Landis says other technologies that will come to the fore in lending will include a new level of robust default management, asset assessment and collections capabilities, which will feed back into the decisioning process. He also says lenders will need to take a more holistic approach to how they extend credit. "Lenders will improve systems and back-office services through consolidation onto multicredit single platforms to cut costs and gain efficiency," Landis notes.

In the end, says BearingPoint's Alcorn, technology can only do so much. "If you set up a program that takes on a lot of risk and something happens to disturb the market, it doesn't matter how automated you are, you will still suffer losses," he observes.


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