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The data requirements needed to manufacture and decision a loan seem to be ever growing, as do regulator and investor expectations. Data is distributed across multiple document types, the loan origination system and a multitude of other sources. All this information needs to be verified and validated to ultimately come to the source of truth and support confidence in loan quality. Unfortunately, this is where the current origination process breaks down and why so many spreadsheets exist in lenders’ processes as a workaround for lack of automation.
While the mortgage industry has grown up around the loan application as the primary source of data, it is becoming abundantly clear that the data and analysis required by investor underwriting guidelines goes well beyond what is collected in the application and in fact comes from many sources. To complicate matters, there is nowhere to enter and store this data in a traditional POS or LOS, nor does the ability exist to ensure consistent use of the information.
This has resulted in an abundance of off-line processes, distributed information and limited access. Critical analysis, calculations, comparisons and the underwriting process itself lack visibility. Transparency to data deficiencies and the ability to understand how calculations, such as debt ratios and income calculations, were derived are virtually non-existent in the decisioning process.
Enter regtech. Regtech can take a variety of forms, but typically it involves business process automation, business rules engines, artificial intelligence, robotic process automation or other automation. The ultimate goal of regtech automation should be to enable electronic verification and validation of thousands of data elements from multiple data sources to create “purified data” that can drive sophisticated decisioning models. Today’s mortgage infrastructure simply can’t support this. For example, a borrower’s income is usually derived from a variety of sources of potentially conflicting data elements; however, there is only one field in most LOS systems for such information.
How does a mortgage banker know whether the income data element that was entered into the LOS is indeed correct? How was it calculated, and what assumptions were made? What other conflicting sources were there, and who verified and validated the data across those sources? Were all the income sources valid and permitted to be used? You can see the problem—and that’s with just one data element.
Through the digitalization of the data acquisition process and the electronic validation and verification of such data against multiple sources, regtech enables greater efficiency, transparency and accuracy while achieving full regulatory and investor compliance. Regtech can also prioritize automation backed by what is now a “super-set” of data from any source, even paper documents. In this regard, regtech solves the “digital divide” that exists between the front-end application process, underwriting work and the loan closing process.
Using fintech and regtech together, mortgage bankers can achieve consistent execution, accurate data and significant reductions in cost, while reducing human error and risk. These are the foundations of completely digital mortgages, which will drive loan quality for the industry.
This content is an excerpt from the LoanLogics white paper, Regtech Explained – Why Regtech is a Necessity for Reducing the Cost of Managing Mortgage Loan Quality. Read more about regtech in the mortgage industry and how it is helping bankers not only comply with regulation but also meet investor guidelines all while minimizing the use of human labor. Download now.
