Residential Magazine

Give the Gig-Economy Borrower a Leg Up

An emerging fintech ecosystem can help to accurately verify income

By Rob Strickland

Traditional loan underwriting processes are designed with the W-2 wage earner in mind. This creates a challenge for gig-economy workers who are commonly ready, willing and able to buy a home.

Thirty-one percent of adults in the U.S. are classified as gig-economy workers — i.e., workers who freelance or contract their labor for a living, according to a 2022 Bankrate survey. The same survey shows that 41% of the people who fall into this category rely on their “side hustle” gigs to help pay various living expenses. But is all of this recurring income available and transparent to a majority of today’s mortgage lenders?

Tapping into these gig-economy workers requires lenders and originators to reevaluate how they validate borrower risk and ability to repay. In the right hands, today’s artificial intelligence (AI) technology will reveal actual spending patterns and available cash flow in real time. Mortgage companies that take advantage of digitized “ground truth” data upstream can drastically reduce instances of fraud, process friction and unnecessary labor further downstream.

Time for reflection

Considering the recent advances in digitized data, AI categorization models and machine learning for intelligent, data-driven workflows, it’s alarming that lenders still rely heavily on layers of human personnel to check and recheck paper-based borrower data prior to approval. As the mortgage industry navigates turbulent macroeconomic conditions, it’s actually a good time to proactively pause, step back and take stock of outdated processes, uncovering ways to apply new technologies that can transform a business.

Many leading lenders are employing better methods to interact with and evaluate all types of borrower profiles. This is especially true for gig-economy workers, who represent a significant underserved market opportunity. Although these detailed self-inspections may appear daunting due to competing priorities, the value of rethinking processes and technology now is likely to yield strong returns as the mortgage market shifts to higher volumes next year. A specific focus on applying AI technologies within the emerging open finance ecosystem can unleash significant value.

Open finance is defined by MX Technologies as “the ability to access and act on financial data to build personalized experiences.” Under open finance principles, consumers use bank-based advanced programming interfaces to share their data with a specific financial institution or fintech platform to unlock potential solutions. Lenders that embrace open finance concepts can harness digital consumer data to deliver more personalized experiences while simultaneously lowering their operating costs.

As an example, a lender can engage a borrower at the point of sale and encourage them to securely share their digital bank data to qualify for a higher loan amount or a lower interest rate. Behind the scenes in real time, high-tech plug-and-play platforms evaluate three, six or 12 months of bank transactions. The goal is to deliver a succinct borrower cash flow pattern that clearly outlines all income streams, recurring liabilities and a precise ability to repay. By leveraging modern technology and rethinking ways to serve clients, lenders and originators can better reach gig-economy borrowers and increase their loan volumes in the process.

Array of benefits

Mortgage lenders that utilize these data-driven solutions will open the door to a myriad of positive outcomes. First, they’ll reimagine the client experience in ways that benefit all parties to the transaction. Serious applicants can be verified in minutes without the collection of paper forms or other cumbersome processes. This digital-first approach also enhances the lender’s brand.

Second, fraud is mitigated as digital channels support seamless cross-referencing of borrower data and ensure a true identity. Third, manual labor costs can be reduced by up to 90% as streamlined digital self-service channels replace phone and email tag. Within this category, the highly valued time of an underwriter is saved as income sources are automatically verified, rendering faster and more accurate decisions for increasingly complex loans.

Lastly and most importantly, many more borrowers can be properly evaluated and their ability to repay can be accurately determined. By embracing these new automated processing and underwriting models, mortgage companies can expand their target markets to include previously underserved borrowers who were ruled out because they could not easily document their recurring gig-income streams.

Although evaluations of emerging technologies should always be a priority, they may never be more important than right now. The mortgage industry needs catalysts to overcome this downward cycle, and applying the right mix of process improvements and AI integrations can be a winning combination. There are companies that will integrate advanced tech tools in record time, accelerating the growth of lending opportunities to gig-economy workers with the requisite financial capabilities. The benefits are irrefutable and there really is no time like the present, so take the plunge, create a plan and find a partner that understands how to unlock the opportunities. ●


  • Rob Strickland

    Rob Strickland is chief revenue officer at VeriFast, an artificial intelligence-powered platform that automates financial analysis and decision making for tenant screening, mortgage underwriting and business lending. Based in New York City, Strickland is an operations executive with 20-plus years of success growing software and professional-services companies. He has a proven track record of leading and inspiring teams to consistently deliver against well-defined market strategies and objectives. Over the past five years, he has provided leadership for sales closings in excess of $100 million in total contract value.

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