TheMReport

26 | TH E M R EP O RT FEATURE N ow that the TILA-RESPA Integrated Disclosure (TRID) rule has been in effect for more than eight months, the mortgage industry has had ample time to draw valid conclusions about the effect TRID is having on the industry—and the picture isn't pretty. Though the "Know Before You Owe" rules may be making the loan process easier to understand for borrowers, lenders are still struggling to comply with TRID in an efficient manner. Time-to-close Delays and Production Costs Skyrocket L enders need to speed time-to- close and ensure compliance with TRID without requiring more labor and increasing loan production costs. To achieve these goals, lenders must leverage technology to automate key steps throughout the entire loan pro - duction process. With automation, labor costs are drastically reduced, turn times are shortened, and compliance is enhanced. Many recent reports have docu - mented the increasing time-to-close and added costs that are results of TRID. The National Association of Realtors recently studied the affect that TRID had on closings and found that the time-to-close for purchase loans increased to a record high of 51 days in January 2016. In another account, the 2016 ABA TRID Survey conducted by the American Bankers Association (ABA) in February 2016 found that 77 percent of respondents reported delays in loan closings, with some delays as great as 20 days. Time-to-close has improved in recent months as the industry has become more familiar with the new closing process under TRID, though the averages are far above the routine 30-day closes of years past. And as lenders add labor to deal with TRID, loan production costs also rise. In fact, the 2016 ABA TRID Survey reported that the average added cost of TRID is $300 per transaction, with some banks reporting as high as $1,000 in additional costs. The increased costs are sweeping, too. According to the MBA's latest Quarterly Mortgage Bankers Performance Report, the first quarter of 2016 saw the second highest level of produc - tion expenses per loan since the inception of the report in the third quarter of 2008. Total loan production expenses increased to $7,845 per loan, up from $7,747 per loan in the fourth quarter of 2015. Personnel expenses averaged $5,141 per loan in the first quarter of 2016, up slightly from $5,131 in the fourth quarter, and up from $4,428 in the fourth quarter of 2014. And, profits are suffering, too. According to the MBA, the aver - age pre-tax production profit was 33 basis points (bps) in the first quarter of 2016, down considerably from the first quarter of 2015 when production profits were at 60 bps. Attacking the Issues Through Automation B y leveraging the right technology and automating time-consuming tasks, lenders can reduce the labor required at key steps along the loan lifecycle by up to 80 percent, resulting in faster loan turn times and increased loan quality. The first major step in this automation process is the on - boarding and setup of a loan. Automated document recogni- tion technology can dramatically speed the onboarding of loans by automatically identifying, naming, and indexing more than 250 com - mon loan documents to create an electronic loan folder. This drastically reduces the time and labor required to name, sort, and compile loan folders. The technol- ogy also provides notification of missing documents, giving lenders the information they need to immediately rectify the problem so they are onboarding only com- plete, compliant loan files. This fast onboarding and set-up of complete, accurate loan packages reduces labor and saves time throughout the entire loan production process. This is especially true in the underwrit- ing step. With automated stacking orders and the assurance that the loan folder is complete and ac- curate upon receipt, underwriters can immediately begin evaluating the borrower loan file to speed the loan production process, rather than chasing down missing documents or manually reorder- ing documents to facilitate the underwriting workflow. Automation also speeds loan evaluation and ensures data integ- rity with a standardized, repeatable process. Automated data extraction (ADE) technology can be leveraged to extract critical data from a num- ber of loan documents, compare values, run the data through a rules engine, and provide alerts on any values that fall outside of es- tablished parameters or tolerances. Only if a document contains a data point that falls outside of the rules parameters would it be sent to a human for review. This exception- based model eliminates the costly and time-consuming "stare and compare" approach and the mul - tiple touches used by many lenders today to ensure data integrity. Automation is the Answer In the face of post-TRID closing delays and rising loan production costs, technology is the industry's best route forward. By Sanjeev Malaney

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