How Lenders Can Streamline Income Verification for Self-Employed Borrowers

The mortgage industry is facing a growing challenge: verifying income for self-employed borrowers, gig workers, and those with multiple income streams. Traditional verification methods, which rely on W-2s and employer databases, often fall short for non-traditional borrowers. As a result, lenders face delays, higher operational costs, and compliance risks.

In a recent interview with MBA NewsLink, Curtis Knuth, CEO of Service 1st, shared insights on the evolving challenges lenders face when qualifying borrowers with gig economy income. He also explored the growing role of AI in the mortgage industry and how technology is shaping the future of income verification.

Why Mortgage Lenders Must Rethink Income Verification in the Gig Economy

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The Challenges of Verifying Non-Traditional Income

Impact on Mortgage Approval Speed

Curtis Knuth: The rise of self-employed and multi-job borrowers complicates income verification for lenders. Lenders usually prefer easy-to-underwrite applications that rely on W-2 income and payroll databases. Yet, gig workers, independent contractors, and small business owners often lack easily verifiable income.

Additionally, lenders encounter challenges when a borrower’s employer does not report to a database verifier or utilize a payroll system linked to a verifier. This creates hurdles for lenders who need to assess stability and compliance while maintaining efficiency. Lenders often must request tax returns and bank statements, resulting in delays and increased manual reviews that add to operational costs and risks.

As self-employment and gig work become more prevalent, lenders must adopt smarter income verification strategies. They can streamline the verifying process by partnering with companies that aggregate data from multiple sources, including IRS tax transcripts, direct deposit records, and bank transaction data.

Leveraging Data for Faster Approvals

Access to diverse data sources enables lenders to validate income swiftly and confidently. Verification providers can implement manual processes when needed. Integrating these solutions into a lender’s loan origination system ensures they fit smoothly into existing workflows. This strategic combination of technology and expanded data access allows lenders to approve loans faster for gig workers and independent contractors while ensuring compliance and reducing risks.

The Issues with Fragmented Verification Products

The Costs of Delays

Curtis Knuth: Fragmented income and tax verification tools hinder the loan process. This is a common issue since many mortgage bankers depend on multiple vendors for income verifications, each with different products that might not integrate well with lender systems. Consequently, lenders often end up chasing documents, soliciting clarifications, and navigating delays.

Automated verification systems provide a more comprehensive overview of a borrower’s income. For instance, when a lender requests a W-2 using our automated Tax Return Verification, they receive details about all employers associated with that taxpayer for the requested tax year. This comprehensive insight eliminates the need to gather separate employment records for borrowers with multiple income streams.

Moreover, allowing access to future years of tax returns ensures transparency for entities that own or service the loan downstream. It also offers borrowers the protection of an FCRA-compliant consumer report.

The Complexity of Tech Integration

Addressing Technical Shortcomings

Curtis Knuth: Connecting solution providers or individual tools is complex. Many tech systems in mortgage banking address specific tasks rather than streamlining the entire loan process. Consumer reporting agencies can provide a range of origination solutions that align with a lender’s chosen LOS. However, many tech vendors see this as an opportunity to charge premiums for access, ultimately raising costs for lenders.

The growing competition in this space, combined with the rapid evolution of AI and democratized data access, will likely reduce costs in the long run. Leading vendors now offer automated verification solutions that help lenders streamline income and tax verification. This includes verification cascades that integrate with a lender’s proprietary technology or preferred LOS, like Encompass’ automated service ordering (ASO). As these technologies gain traction, they enhance workflows and lower verification expenses, regardless of a borrower’s situation.

Enhancing Efficiency with Advanced Verification Technologies

Streamlining the Verification Process

Curtis Knuth: Lenders gain significant advantages from verification solutions that centralize and streamline income and tax verification processes. For example, verification technology that enables direct access to IRS tax data allows lenders to reliably confirm self-employment, rental income, and other non-traditional earnings. Innovations exist that can process tax transcript data alongside VOE data through a rules engine. The output includes validated income figures along with underwriting messages, ensuring securing future loan releases.

Preventing fraud is crucial when verifying taxes for self-employed borrowers. Transcripts from IRS Form 8821 reveal a borrower’s address, income, and Taxpayer Identification Number (TIN) recorded by the IRS. Lenders can use this data to validate information provided on the application, confirming SSN or Individual Taxpayer Identification Number (ITIN). This protects against misrepresentations of a borrower’s financial status. Automated ordering processes contribute to handling verification requests efficiently without unnecessary manual bypasses.

Verification cascades facilitate lenders in requesting data from various sources automatically based on predefined criteria, reducing manual intervention and costs. Automated service ordering (ASO) enables lenders to order multiple verification products in an organized workflow, which enhances consistency in income validation. By utilizing automated workflows and structured integrations with tax and income data sources, lenders can expedite application processing while ensuring accuracy and minimizing costs.

The Future of Income Verification

Using AI for Consumer Insights

Curtis Knuth: AI and machine learning tools are exceptionally suited for unconventional mortgages that involve borrowers with non-traditional income. Conventional underwriting models often rely on fixed guidelines suited for W-2 employees, which may not work well for self-employed individuals or gig workers.

AI-driven solutions enable lenders to analyze a borrower’s financial history efficiently, enhancing both speed and accuracy. One of AI's major advantages is its capability to process extensive data at high speed. Machine learning models can evaluate bank transactions, tax records, and payroll data to develop a detailed financial profile for borrowers. By employing AI, lenders can identify income trends, detect inconsistencies, and calculate stable monthly income with improved precision.

When selecting vendors, lenders should ensure that services are deliverable in a data format like .json. Safe and secure environments for storing consumer data are imperative to uphold compliance.

Evaluating income and employment data is crucial, especially for self-employed borrowers, as their earnings can fluctuate. The industry needs to remain cautious about how AI is applied during borrower qualification. However, as these technologies further develop, lenders will be equipped to make timely, well-informed lending decisions without sacrificing accuracy or compliance.

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Conclusion

The lending landscape is evolving. Mortgage bankers must adapt to meet the needs of non-traditional borrowers. Embracing advanced income verification technologies will not only improve efficiency but also lower costs and mitigate risks. As AI and machine learning continue to transform the industry, lenders leveraging automation and real-time data access will be best positioned for success. Modernizing income verification strategies enables the mortgage industry to achieve faster approvals, greater transparency, and a more inclusive lending process for all borrowers.