When it comes to mortgages, accurate data is non-negotiable. It’s simply impossible to maintain regulatory compliance or confidently sell loans on the secondary market without it.
However, ensuring data accuracy can be a daunting task. It takes a tremendous amount of time and effort to review loan documents and check and cross-check data manually. At the same time, the traditional technologies lenders and servicers have used to identify and capture data from loan documents are extremely limited.
Adding to this data dilemma are two recent market developments that have made it even harder for lenders and servicers to ensure data accuracy. Originators are facing new QC requirements from Fannie Mae, while the hot MSR market and the growing potential for increased defaults is placing pressure on servicers to get data right. But as the French playwright Molière once noted, “The greater the obstacle, the more glory in overcoming it.”
The Latest Push for Loan Quality
Quality data demands on lenders rose significantly this year when Fannie Mae introduced new pre-funding and post-closing QC requirements that are expected to go into effect next month. The GSEs’ new policies require lenders to complete a minimum number of pre-funding QC reviews every month, plus lenders must now complete their post-closing QC reviews in 90 days instead of 120 days.
While there has been some pushback on Fannie’s new requirements, they underscore the critical need lenders have for tools that enable them to quickly identify incorrect or missing data that could lead to loan defects and potentially costly repurchases. It also bears mentioning that Fannie Mae is far from alone in demanding higher loan quality. Investors and federal regulators are increasingly requiring it, too.
Invariably, this means lenders need accurate data extraction technology that enables them to instantly identify, capture and extract data from any loan document. The problem? Most third-party providers of data extraction technology and legacy optical character recognition (OCR) tools have failed to keep up with the growing number of structured and unstructured loan documents. Some providers have been cutting staff in response to higher rates and lower loan volumes, too, so the problem continues to persist.
Servicers Have Their Own Challenges
The need for accurate data is not limited to lenders. On the servicing side of the business, high quality data is equally paramount—and growing more so.
One reason for this has been the growing demand for MSRs, as many organizations have pivoted to servicing loans to counter decreased loan production. Ensuring accurate data is critical to quickly ingesting and analyzing loan files and auditing loan documents before integrating them into a servicer’s system.
Data accuracy will grow significantly more important if mortgage defaults rise, too. Such a development is looking increasingly likely, since many new homeowners have found themselves stretched thin financially due to the recent rise in interest rates and inflation. In order to perform loss mitigation processes efficiently, including forbearance requests or loan modifications, servicers will need to ensure all existing loan data and the data from incoming borrower documents is accurate.
Unfortunately, many data extraction technologies have proven no more effective for servicers than they have for originators—either because they are built on older technology or because their solutions are unproven. And ensuring data accuracy when exchanging MSRs or determining the proper loss mitigation option for a borrower in default is equally as intimidating when performed manually as it is for originators. Not only do manual processes create delays and potential errors when onboarding loans, but they also add to a servicer’s costs in what is already a cost-intensive business.
Harnessing the Power of AI
With the proper technology, any mortgage organization can ensure it is working with accurate data and is able to seamlessly identify potential issues before they become expensive problems. The right technology can also help lenders identify mismatched data and use it to help spot fraud.
The key lies in leveraging modern, proven AI-powered data extraction technology that enables companies to effortlessly classify and extract data from diverse structured and unstructured documents with speed and precision. That is why an increasing number of lenders and servicers are turning to Paradatec’s award-winning AI-Cloud solution.
Equipped with an impressive pre-built library of over 8,500 unique data fields found in loan documents, AI-Cloud harnesses advanced machine learning capabilities to swiftly and accurately transform the information trapped within documents into individual data elements. And it does so extremely accurately and reliably, so that clients can generate purified data to fuel their automation initiatives and gain valuable insights into their processes and strategic plans.
Because it can pull data from over 850 known mortgage document types within seconds, AI-Cloud also empowers clients to confidently audit large volumes of loan files to meet Fannie Mae’s new requirements and post-closing deadlines. It also enables servicers to automatically audit data provided by prior servicers against incoming documents before ingesting MSRs into their system, thus minimizing errors and enhancing their overall customer experience.
If recent market developments have you concerned about data accuracy, we can help. Just drop us a note at firstname.lastname@example.org!