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Changing economic conditions, their effect on consumer behavior and related shifts in credit risk are driving efforts to provide credit scoring models with a more predictive risk assessment edge.
Davies argues that to be able to commensurate with “a significant change in consumer credit repayment behavior” all credit models should be updated regularly. VantageScore 2.0 replaces an earlier version first introduced in
VantageScore 2.0 retrieves data from the 2006 to 2008 and 2007 to 2009 performance timeframes, each representing 50% of the data sample.
According to VantageScore president and CEO,
At present one growing concern for the mortgage industry is the scoring of seriously delinquent borrowers who are 90 days or more past due on their mortgage.
LPS reports that these “extremely delinquent” loans reported in
VantageScore 2.0 offers predictive analytics about customers who most likely will default on their loans or become 90 days or more delinquent. One of the advantages is that it features “the exact same algorithm used by the three credit score bureaus,” Davies says, consequently risk assessment is more consistent. “The only reason why a customer would get a different credit score is if the underlying data is different at the credit bureaus.”
Data can be different for various reasons, including filing errors, credit bureau differences in data interpretation, or discrepancies in the time period lenders report data to credit bureaus. In addition, some other credit scores are based on very different algorithms that change the final interpretation of the information along with the results.
(As a rule mortgage servicers rely heavily on statistics from at least two credit scores from different credit bureaus to ensure higher accuracy.)
Right now lender-servicers are looking for accurate, consistent interpretation of the customer’s risk, a credit score that really captures what is going on in the economic environment, and eliminates the need to use credit score averages to assess risk, she says.
Using a standard algorithm helps produce more consistent results as much as the size of datasets. Large data pools, such as VantageScore’s 45 million customer files help capture “all the variations in customer behaviors.” Davies stresses, however, that there is one risk: data accuracy is very time sensitive so even a one-year-old dataset can be ineffective.
Research finds have shown more than one example of how customer behavior has changed. As difficult economic times weigh in, some customers are switching their priorities away from their mortgage. A review of delinquent consumer data from 2006 to 2009 shows this behavior shift has affected 1% of the overall U.S. population. Nonetheless it represents a 90% increase in the number of customers who are late on their mortgage but current on credit card and auto loans.
She argues that this finding warns the financial industry-especially servicers-that they need to update their credit risk management models. Servicers need to inquire whether they are able to valuate this shift, whether “this is a systemic and permanent change,” or something that will eventually disappear as customers revert back to what has traditionally been their normal behavior of keeping their mortgage current first and foremost. It is hard to foresee, Davies says, because the reasons why people are doing that vary. But it definitely is a trend worth monitoring for real estate risk management purposes.
Another “pretty obvious” risk management related finding in her view is that currently customers who are looking for new credit tend to be very high credit risk borrowers “in severe need” of credit. “At this point inquiries and newer trade lines are often signals that the customer is potentially exposed financially beyond what they are able to handle.” So it is more of “a red flag” than it would have been during more normal economic times.
The third emerging issue in customer behavior that has not been researched enough as of yet, is borrowers’ reaction to modified mortgage loans. Since the mortgage industry set up certain codes that would allow the identification and monitoring of government modified loans as late as
The hope is that mortgage servicing risk will improve the same way mortgage loan origination risk has improved. Overall, origination risk dropped by 30% compared to last year even thought it is still very high compared to five years ago. The effect of good risk management and disciplined planning played out well with originations, she says. Now the key is to ensure the same happens with all the matured loans on the books and is applied onto the entire credit spectrum. VantageScore 2.0 is available for testing on consumer data from