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.
MBA NewsLink: How is the growing number of self-employed and multi-job borrowers affecting the speed and accuracy of mortgage approvals, and how can lenders streamline the process for these borrowers?
Curtis Knuth: The rise of self-employed and multi-job borrowers is making income verification more complex for lenders. Easy-to-underwrite applications rely on W-2 income and payroll databases, but many gig workers, independent contractors, and small business owners don’t have that kind of easily verifiable income.
Additionally, lenders face coverage challenges when a borrower’s employer does not report to a database verifier or use a payroll or HR system linked to a verifier. That creates challenges for lenders who need to assess stability and ensure compliance while keeping the process efficient. Typically, lenders end up having to request tax returns, bank statements, and other documents from the borrower, which leads to delays and manual reviews that add costs and risks.
As self-employment and gig work becomes more common, lenders need smarter income verification strategies. They can streamline the verification process by choosing partners that aggregate data from multiple sources, including IRS tax transcripts, direct deposit records, and bank transaction data.
Having access to a wide range of data sources allows lenders to validate income quickly and with greater confidence. Verification providers can also use manual processes when necessary. The key is integrating these solutions into a lender’s loan origination system so that they work efficiently within existing workflows. With the right combination of technology and expanded data access, lenders can approve loans faster for gig workers and independent contractors while maintaining compliance and reducing risks.
MBA NewsLink: How do fragmented income and tax verification products impact mortgage bankers when working with borrowers with non-traditional income sources?
Curtis Knuth: Fragmented income and tax verification tools slow down the loan process and make it harder for lenders to approve borrowers with non-traditional income. This is a common issue since many mortgage bankers rely on multiple vendors for income verifications, each offering different products and services that may not integrate well with existing lender systems. As a result, lenders often end up chasing documents, requesting clarifications, and dealing with delays.
Today’s automation provides a more complete picture of a borrower’s income. When a lender requests a W-2 through our automated Tax Return Verification, for example, they receive all the employers tied to that taxpayer for the requested year. It’s a complete, comprehensive picture. For borrowers with multiple income streams, this eliminates the need to track down separate employment records.
Also, authorizing access to future years of tax returns ensures transparency for entities that own or service the loan downstream and provides the borrower with the protection of an FCRA-compliant consumer report.
MBA NewsLink: Why aren’t most tech systems helping mortgage bankers reduce costs for borrowers? How does advanced income and tax verification technology speed up the loan process and lower operational expenses?
Curtis Knuth: It is highly complex to link solution providers or individual solutions together. Many tech systems in mortgage banking were built to handle individual tasks rather than streamline the entire loan process. Consumer reporting agencies have the advantage of offering an array of origination solutions that integrate with a lender’s LOS of choice. However, many technology vendors recognize this as a business opportunity and often charge a premium for access to these solutions, which ultimately raises lender costs.
I believe the growing competition in this space, the incredibly rapid growth of AI, and the democratization of data access, will drive costs down. Top vendors already provide automated verification solutions that help lenders streamline income and tax verification. These offerings include verification cascades that integrate with an originator’s proprietary technology or chosen LOS, such as Encompass’ automated service ordering (ASO). As these technologies gain traction, workflows will become more efficient and verifications less expensive, regardless of a borrower’s scenario.
MBA NewsLink: What specific features of income and tax verification technology can improve efficiency for mortgage bankers, especially when working with borrowers with non-traditional income documentation?
Curtis Knuth: Lenders benefit from verification solutions that centralize and streamline income and tax verification. For example, verification technology that provides direct access to tax data from the IRS enables lenders to confirm self-employment income, rental income, and other non-traditional earnings more reliably. There are also tools that can ingest tax transcript and VOE data and run it through a rules engine. The final output includes a validated income figure and corresponding underwriting messages, with an insurance policy for the loan set to be released soon.
Fraud prevention is also a key component in tax verification for self-employed borrowers. Transcripts from IRS Form 8821, for example, show the borrower’s address, income, and Taxpayer Identification Number (TIN) as recorded by the IRS. Lenders can use this data to validate the same information provided on the application, such as SSN or Individual Taxpayer Identification Number (ITIN). This ensures the authenticity of the borrower’s financial status and prevents misrepresentation. Automated ordering processes can also ensure that verification requests are handled efficiently without unnecessary manual steps. However, some of the key differences between providers are the speed, integration capabilities, and the level of customer support they offer when delivering tax transcript data.
Verification cascades allow lenders to request data from different sources automatically based on predefined criteria, which reduces manual intervention and costs. Automated service ordering (ASO) enables lenders to order multiple verification products in a structured workflow, improving consistency in income validation. By leveraging automated verification workflows and structured integrations with tax and income data sources, lenders can process applications faster and more accurately while reducing costs.
MBA NewsLink: How can AI and machine learning help mortgage bankers more effectively assess non-traditional borrowers and make quicker, more accurate decisions?
Curtis Knuth: AI and machine learning tools are a perfect fit for non-conventional mortgages that involve borrowers with non-traditional income. Mainstream underwriting models rely on fixed guidelines that work well for W-2 employees but struggle with self-employed borrowers, gig workers, and those with multiple income sources. AI-driven solutions allow lenders to analyze a borrower’s financial history more efficiently, improving speed and accuracy.
One of AI’s biggest advantages is its ability to process large volumes of data quickly. Machine learning models can evaluate bank transactions, tax records, and payroll data to create a comprehensive financial profile of the borrower. Instead of manually comparing tax returns and bank statements, lenders can use AI to identify income trends, detect inconsistencies, and calculate stable monthly income more accurately. When evaluating vendors, lenders should make certain the services they request can be delivered in a data format like .json, and that they have a safe and secure environment to store that consumer data, which is highly critical.
Evaluating income and employment data is particularly useful for self-employed borrowers, whose earnings fluctuate and require a deeper analysis. Of course, the industry must be mindful of how AI is applied when qualifying borrowers. However, as these technologies continue to advance, lenders will be able to make faster, more informed lending decisions without sacrificing accuracy or compliance.
Conclusion
The lending landscape is evolving, and mortgage bankers must adapt to meet the needs of non-traditional borrowers. Embracing advanced income verification technologies will not only improve efficiency but also drive down costs and mitigate risk. As AI and machine learning continue to shape the industry, lenders that leverage automation and real-time data access will be best positioned for success. By modernizing income verification strategies, the mortgage industry can move toward faster approvals, greater transparency, and a more inclusive lending process for all borrowers.
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