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Mortgage Fraud News | Fraud Statistics | Fraud Index | Fraud Blog
Mortgage broker, originator and banker criminal cases. Real estate flipping and appraisal fraud. Mortgage fraud prevention, studies and analysis.

Eliminating Mortgage Fraud Losses

BasePoint Analytics releases White Paper

January 29, 2008


A fraud prevention company has released a White Paper that discusses how mortgage companies can reduce losses resulting from mortgage fraud.

The report, Rebuilding the Mortgage Industry - the Path Back to Profitability, was released by BasePoint Analytics LLC yesterday.

Mortgage bankers need to identify misrepresentations as "fraud," instead of just ignoring them and approving the loan anyway. Stated-income loans originated between 2004 and 2006 contained unreasonable incomes from borrowers that were motivated to buy their dream homes -- and these loans should have been declined.

"For the industry to move forward there needs to be a common recognition that fraud is occurring and that fraud that was suspected over the last few years, funded anyway and not disclosed to the investment community, has contributed greatly to the default problems causing the so called credit crunch today," the paper stated.

BasePoint said that sales became more important than risk management and prudent underwriting. The company called on originators to focus more on the role of underwriting in approving good loans while avoiding unacceptable risk. In addition, a process should be established to handle and, when relevant, challenge repurchase requests.

Lenders should establish an entire fraud management team that understands, measures and controls fraud losses. This team should be aware that as underwriting standards become tighter, fraud perpetrators become more sophisticated in beating the system.

For instance, as state-income loans are eliminated, the use of forged income and asset documents increases. Lenders should better analyze the likelihood that a particular borrower could make the stated level of income.

Another example is an increase in overvalued appraisals to offset lower available loan-to-values. One way to protect against inflated values is by using data accumulated through a consortium of companies that share data.

BasePoint explained that increasing credit score requirements for subprime borrowers can actually result in higher defaults because the segment of borrowers lost was less likely to default than borrowers who can still qualify by artificially boosting their credit scores. If those borrowers actually had the higher scores, they would have been able to qualify for prime products.

"Curtailing good loan volume through dramatic and overarching policy changes can exacerbate the profit squeeze in the short-term," the report stated. "Lenders should adopt more targeted, data-driven tools to retain the good origination volume, while substantially reducing mortgage fraud and EPD risk."

Most mortgage brokers submit valid loan packages. Most of the fraudulent files submitted as well as most of the early payment defaults are the result of just 10 percent of a wholesaler's brokers. A correlation exists between the fees and points charged by a broker and the likelihood of material misrepresentation -- an area wholesale lenders can easily monitor.

BasePoint noted that fraud prevention technology like what it provides helped the credit card industry to reduce fraud by around 70 percent since 1993, when fraud rates were accelerating at a tremendous rate. Pattern recognition technology was responsible for much of the decline.

"Most traditional fraud products in the mortgage industry are based on comparisons between application information and third-party data sources. Data validation will still have its role, but it's obvious from the current state of delinquency rates and defaults that these practices alone are not sufficient," the report said. "Fortunately, there is a proven predictive analytic technology designed specifically to curb mortgage fraud and early payment default."

BasePoint claims that by reviewing just 10 percent of loan applications, its predictive models can identify at least 40 percent of loans that would result in early payment defaults without throwing out loans with no fraud.

Sam Garcia worked in mortgage lending for twenty years prior to becoming publisher of

e-mail: [email protected]

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