Arvin Wijay is CEO and founder of Retreat Capital Management, a loan modification and REO management company. Arvin also served with Morgan Stanley [stock MS][/stock] as an executive vice president in production and strategic management. In this role, Arvin was an integral part of the acquisition, for $706m, of Saxon Mortgage. Upon completion of the acquisition he was responsible for transforming this small mortgage company and servicer to a Wall Street quality company. If you’re a smaller servicer, what do you need to do to not only survive the new and drastic changes in the default space, but also make a profit? To survive and be profitable, smaller servicers must identify and focus on their core function in the industry. Are you a primary or specialty servicer? Even with the best employees, small servicers can’t do everything, particularly in the current environment with its rapid changes in consumer behavior and legal and infrastructure requirements. Any tasks and activities that are temporary in nature, which are not adding core value, need to be outsourced in order to create the greatest efficiency company-wide. Outsourcing allows smaller servicers a cost- and time-efficient way to access expertise, processes and technology. Focus and smart spending will maximize efficiency and lead a smaller servicer to survival and profitability. What sort of predictive modeling are servicers and banks using to forecast foreclosure figures? And how are they being used to court fresh blood from the secondary side? Although there is variability in the level of sophistication, banks and servicers are relying primarily on standard roll rate and regression models to forecast foreclosure figures. Generally, their models incorporate mortgage data and generic credit information – such as FICO scores – to get a rough estimate of delinquency progression and frequency and timing of foreclosure. How different is that from the past? It was acceptable to know generically if borrowers paid their bills. In today’s environment, however, effective performance modeling can’t rely on a generic payment score. To be successful now, banks and servicers need to know if a borrower will make their mortgage payment. To accurately predict mortgage payment behavior, models must capture the higher order interactions that exist among granular borrower credit information, mortgage data and demographic information. By integrating predictive models with these attributes, servicers will have an effective, proactive default management tool. Proven results will have secondary market investors courting servicers rather than servicers courting secondary market investors. Has the foreclosure crisis, in a way, sparked a hiring spree – at least in the servicing space? Yes, most definitely. A lot of servicers have responded to the added volume and activity by hiring more people. The issue is that the solution isn’t simply hiring people – it’s about taking the time to profile the type of people then focusing on hiring and training the right people for the task. We’ve heard of some of the larger servicers hiring ex-McDonald’s employees to help modify loans. How important is it for these servicers to hire quality people and why? It’s absolutely critical to hire the right people. Loan modifications are a universe unto themselves. Experience isn’t the only important issue. There’s also training and internal skill sets. Hiring ex-McDonald’s employees isn’t the issue, even though facilitating loan modifications is a lot different from making hamburgers. Any individual hired, whether a former employee of McDonald’s or not, needs to have the experience, skills, temperament and training in order for a servicer to be successful. If loan modifications aren’t done the right way – if there is some error in processing or underwriting – the outcome could have a huge impact on financial risk, not only for the servicer, but for the investor and borrower as well.

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