Lenders, servicers and third-party service providers are now focusing on processes. Inconsistent, manual or expensive platforms that do not assess a mortgage loan portfolio in its entirety are a thing of the past.
“Market shifts and regulatory requirements have made portfolio management more crucial and complicated than ever,” said president of DataQuick John Walsh. “Identifying problems, opportunities and deploying a custom solution for each loan is imperative to not only ensure compliant management, but also to maximize revenue and profit potential.”
DataQuick, a real estate data and analytics technology provider, has developed Portfolio Management Intelligence Suite. The software is designed to identify “credit, collateral, lien and transaction risks” in a mortgage loan portfolio. Users can customize features according to their unique business rules to determine individual property risk. The suite provides cost-effective loan reviews that allow for more frequent evaluation of the portfolio based on four core components that can be used together or individually in different setups. National Property Database, Precision Lien Model, ARTAdvisor and PortfolioQ address lien data, lien positioning logic, automated credit and lien analysis, and automated analysis.
Users are looking for loan-level and portfolio management decision making based on transaction history, sophisticated logic, rules-driven automated analysis, Walsh says, to maintain “a complete and accurate picture of all liens on a property.”
Other mortgage technology providers are developing solutions that enable users to implement more granular loan portfolio analytics. VantageScore Solutions has reconstructed its credit scoring mode both to aid lender implementation and consumer understanding.
VantageScore 3.0 model uses a 300 to 850 valuation scale and higher predictive accuracy. The new and expanded scoring scale enables lender and servicer users to formulate a score for 27 million to 30 million previously unscoreable consumers.
According to the Stamford, Conn.-based company whose generic credit scoring model has been used by the financial services industry since March 2006, VantageScore 3.0 provides “up to 25% predictive improvement over earlier models.”
The new and inclusive approach, said president and CEO of VantageScore Solutions, Barrett Burns, was deemed necessary in today’s competitive lending environment that “dictates that lenders need access to as many creditworthy consumers as possible within their target universe.”
The VantageScore 3.0 model is designed to facilitate lending risk management and data reporting for lenders and servicers, as well as ensure consumers can use information “to become better managers of their own credit,” Burns said. Hence, VantageScore 3.0 “is both a new model and new path forward” for the credit scoring industry. To aid lender implementation and consumer understanding VantageScore has also reduced the number of reason codes to less than 80 and simplified the language used.
Preliminary tests show highly predictive credit scoring across industries and applications, “in particular within the key prime and near-prime consumer populations,” executives said, enabling banks “to extend credit to tens of millions of consumers that were previously invisible to them.”
Times have changed, agrees DataQuick’s Randy Wussler. "It used to be just a credit decision, all revolved around credit, now collateral exposure is much more important."
“What we’re finding though is that it’s really important to be able to marry those two, to be able to deliver an integrated view of the borrower, a view of the property, not just from a credit standpoint, not just from a lien or collateral standpoint, but all of that integrated together. More and more our lender-servicer customers are telling us that they need that integrated solution.”
The reason is simple, he explains, bankers cannot make an educated decision on the loan or the portfolio in general unless they have all the data.
Usually the challenge for servicers is to report on data they do not have or even be aware of data gaps that can seriously influence mortgage servicing business analytics. As to how complicated that process is, Wussler says, “a couple of folks we’ve talked to have said, 'I’m just going to use credit information. I’ll pull credit.'”
The problem with that approach, he says, is that tying credit to specific properties can be a challenge in itself.
A loss mitigation professional may be able to see that there are two mortgages under the same borrower’s name, for instance, but the collateral is different. “We always try to encourage folks to use solutions that pull evry bit of information together, under the same roof, that delivers an integrated view of the property risks, which typically is not something a servicer can process in-house and often has to turn to experts on the outside to pull all of that together.”
Different servicers make different choices, he says, but as time goes on, the regulatory environment just gets way too complicated for servicers to rely only on one single source. “That’s just asking for trouble!”