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NAMB says GAO foreclosures study vindicates brokers: Aggressive lending, securitization model led to lower underwriting standards

National Mortgage Professional
Nov 25, 2007

Pooling data to catch mortgage fraudFrank McKennapredictive analytic models, create statistical norms and measure deviations, exploit links and anomalies Lenders joining fraud data consortium to uncover patterns of fraud For years, many sectors of the financial services industry have relied on sharing information on fraud patterns and trends to detect fraud earlier. Banks, insurance companies, cellular service providers and credit card companies have each pooled their data into consortiums in an effort to find those fraudsters who reuse information over and over again in their schemes. This information can include stolen identities, false employment information, mail drop addresses, stolen bank account numbers, check information, cell phone numbers and other personal data. By pooling data, these companies have been able to get ahead of the fraud schemes that are being perpetrated against them. Mortgage companies are increasingly relying on this data-sharing approach to help them identify some of the largest and most egregious fraud schemes as early as possible. A mortgage fraud data consortium effort is now underway for mortgage lenders and investment banks. The consortium enables them to pool their information to help detect mortgage fraud before funding and before loans are purchased on the secondary market. The consortium acts similarly to a credit bureau or other data aggregator, providing a central repository of information. A credit bureau, for example, collects and reports on credit and payment data for many different financial institutions. Lenders and creditors submit information on their borrowers' behavior each month, and that data is aggregated to be used to understand each borrower's ability to repay a loan through analytics such as credit scoring. It is much the same way that lenders can submit their data relating to fraud trends to enable fraud scoring on future mortgage applications and loans. In a mortgage fraud consortium, lenders and investment banks submit their origination information on all applications and loans, as well as data on loan performance, indicating loans that performed well, defaulted or had fraudulent misrepresentations. This data, aggregated across many institutions, is used to build analytic models to be used by consortium members to fight fraud. In addition to all applicant and performance data, the consortium also includes broker information, appraiser data and loan program details. Why should lenders join a mortgage fraud consortium now? There are four key benefits to being a member of a mortgage fraud consortium: 1. Access to predictive analytic models These models are built on the robust consortium data from numerous lenders and investment banks, and are highly accurate in predicting significant misrepresentations that would result in losses if the loan were booked or purchased on the secondary market. Having this depth of information gives the ability to very quickly understand how different parties involved in a transaction might perform with other lenders. 2. Ability to create statistical norms and measure deviations Lenders in a fraud consortium benefit from sharing information on applicants and creating statistical norms related to fraud that help them easily and quantifiably understand normal versus abnormal information. For example, if the median income in a given geographic area is $7,000 per month and a borrower reports an income of $15,000 per month in the same area, the lender is notified of an abnormality, which it may choose to investigate further. When members of the consortium aggregate data, they have a larger pool with which to compare average values and identify deviations of these statistical norms to help them prevent potential mortgage fraud. 3. Models built on more data are more robust When building an analytic model based on five or 10 million loans instead of 50,000 loans, more patterns of fraud are found. This translates into more powerful detection capability. 4. Ability to exploit links and anomalies Consortium members can use link analysis to effectively identify mortgage fraud. Link analysis is the ability to detect a piece of information that has been involved with several fraudulent loans. For example, if an employer's phone number or a tax preparer are involved in several non-performing or fraudulent loans and that phone number comes up in another application, the file can be flagged to let the lender know that the information has been involved in a prior fraud. The ability to create these links in the data becomes more robust, as lenders and investment banks share data through the consortium. Why is an independent data aggregator important? If lenders were to attempt to share data on their own without an independent data aggregator, there would be some challenges, mainly relating to collecting data. These would include: -Lenders and investment banks might not know which data is most important to collect to predict and identify fraud, and how long it should be kept to build the most robust predictive model. -Mortgage lenders haven't always collected and stored historical data digitally, since it has traditionally been a paper-based environment. -Lenders and investment banks are hesitant to share sensitive data with each other directly, since information such as their number of fraudulent and defaulted loans is proprietary. These challenges are quickly alleviated by working with an independent third party to build and manage the consortium. By doing so, lenders and investment banks quickly gain the ability to detect fraud early, because they can see trends that are obvious in the consortium, but not in their data alone. Frank McKenna is co-founder and chief fraud strategist at BasePoint Analytics, a Carlsbad, Calif.-based fraud analytics and consulting company serving the mortgage and banking industries. He may be reached at (760) 602-4971 or e-mail [email protected]
Published
Nov 25, 2007
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