Manually Scrubbing For HMDA Compliance? It’s Time To Automate

Investing in digital transformation systems provides a significant advantage over “wait-and-see” institutions

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Chief Revenue Officer

The compliance landscape is rapidly changing, with more regulations such as the Dodd-Frank 1071 ruling released in March 2023. Paired with the already intense scrutiny of HMDA data, institutions are feeling the pressure of keeping up with data volume and integrity demands. Lenders and banks have to look hard at their systems, staff, and processes to make sure they can meet these requirements.

The biggest challenge keeping leaders up at night is how HMDA requirements will affect labor costs and their ability to keep high data integrity standards. While examining existing systems and manual scrubbing procedures, financial institutions have realized they are largely underprepared for 1071’s level of scrutiny.

While they have been using automation tools in processes such as credit increase approvals, drive-through tellers, and AI customer service centers, the mortgage compliance processes have been left largely untouched by automation. Institutions must mitigate increased margins and overhead due to too much manual administrative labor.

To deliver accurate and consistent lending data amid increasingly restrictive compliance guidelines, leaders are turning to machine learning systems to fulfill their compliance tasks.

Labor Costs

With increasing regulation in addition to existing HMDA guidelines comes a pressing need for compliance experts to sift through loan data and collect over 110 data points, ensuring all documents are properly accounted for, audited, and verified. Though crucial for providing quality data, these tasks are often monotonous, time-consuming, and fraught with human error. Compliance affects loan origination costs, and cost of origination is very closely tracked, more so than other compliance costs. Since this data needs to be collected at the beginning of the loan process — regardless of if the loan is funded — this is costing the loan origination process from the beginning.

To keep up with demand, many financial institutions are anticipating having to increase their compliance department budget to comply with new regulations, which will skyrocket labor costs at a time when lending has slowed down. Additionally, increasing staff does not guarantee data integrity, which is crucial.

Machine learning offers a solution. Instead of having dozens of experts perform menial tasks like data scrubbing, machine learning systems can quickly and efficiently automate document processing, extract data automatically from verified documents, and eliminate manual application verifications. An efficient automation system, easily embedded into existing digital onboarding experiences, can also root out fraud false positives and eliminate quality issues associated with outsourced or offshore teams. These manual processes, which usually take teams too long to accomplish or provide subpar data, could be completed to standards in a fraction of the time.

Frequent errors are not tolerated and must be curbed to thrive in the competitive financial landscape.

As a result, implementing machine learning tools can dramatically reduce staffing costs while increasing loan margins and allowing compliance professionals more time to concentrate on high-level, profit-producing ventures. Automation takes care of time-consuming and repetitive tasks, which helps leaders avoid staffing headaches from quarter to quarter while guaranteeing consistent data.

However, machine learning is not something that can be implemented overnight, especially if the necessary framework is not put in place. At a time when margins are being squeezed from every direction, reducing labor costs by investing in digital transformation systems as soon as possible will provide a significant advantage as more traditional, “wait and see” institutions struggle to keep up.