Income Verification and the Payslip Paradigm: The Last Bastion of Document-Based Credit Decisions?
By Mike Page, CEO of MOGOPLUS
As global interest rates begin to normalise, 2025 is expected to bring a surge in lending activity across various sectors and credit products. While many lenders are shifting toward more data-driven approaches to manage risk, one crucial area of credit decisioning remains firmly rooted in outdated processes: Income Verification.
The Age-Old Process: Payslips and Manual Calculations
Understanding an applicant’s income is the cornerstone of all credit risk assessment models. Yet, despite advances in data analytics, AI, and machine learning, many lenders—including some of the largest banks in Australia and New Zealand—still rely on traditional, 30-year-old methods to verify income. The standard process typically involves requesting pay slips (or pay stubs, as they are called in North America) from applicants.
Once received, these documents are manually downloaded, reviewed, and annotated, with the figures then entered into an origination platform or serviceability calculator—often by a human. This labour-intensive approach not only introduces unnecessary delays but also increases friction for applicants, which runs counter to the modern demand for seamless customer experiences.
The Limits of the Payslip Paradigm
For lenders trapped in the “Payslip Paradigm,” the growth of their loan books is directly limited by the capacity of their teams to process these manual tasks. The cost of origination remains high compared to more data-focused competitors, and the efficiency of their operations suffers.
Moreover, asking applicants to provide payslips and annual salary statements adds friction to the application process, discouraging some borrowers from completing their submissions. This outdated method not only frustrates customers but also hinders the lender’s ability to scale efficiently.
The Irony of Trust: Payslips vs. Banking Transactions
A key barrier to adopting more modern, data-driven approaches is a perceived lack of trust in income data sourced directly from applicants’ bank transactions. Ironically, this skepticism persists despite the growing ease with which payslips—simple documents that can be easily forged using free software—can be manipulated. On the other hand, transaction data is almost impossible to falsify.
While consented data sharing through initiatives like open banking may not be suitable for all applicants, its security and convenience are no longer in question. With clear, upfront user experience messaging, consented data can achieve completion rates in the 90th percentile, ensuring high levels of engagement and data accuracy.
Intelligent Data Insights: Beyond Simple Income Verification
One concern frequently raised by lenders is the challenge of separating regular income from non-regular credits such as commissions or bonuses. This distinction is crucial because lenders often treat these types of income differently when assessing serviceability.
Here, intelligent insights and pattern recognition analytics can step in. By categorising income data into regular and non-regular streams, lenders can quickly and accurately assess an applicant’s true financial situation—without relying on complex AI algorithms. This approach ensures faster decision-making while still maintaining accuracy.
The Gig Economy: A Growing Blind Spot for Traditional Models
Traditional credit decisioning processes are also ill-suited for the growing Gig Economy, where workers earn income from multiple sources. The current system, which generally caters only to single-employer, PAYG (pay-as-you-go) situations, often overlooks secondary income streams, particularly when it requires processing multiple payslips from different employers.
According to the Australian Bureau of Statistics (ABS), the number of gig economy workers in Australia grew by 55% between 2012 and 2019, reaching over 1.4 million individuals. While more recent data is pending, this trend is expected to have continued. In the United States, approximately 33% of workers are now part of the gig economy, according to the McKell Institute.
For this growing cohort, a data-driven approach to income verification—one that can aggregate income from multiple employers or institutions—will simply allow credit providers to lend to more customers. By leveraging data from bank transactions, lenders can accurately assess the total serviceable income of gig workers, avoiding the limitations of manual payslip processing.
The Future of Income Verification
As customer expectations evolve and the credit market enters a new phase, the question remains: will traditional income verification methods, like payslips, remain relevant beyond 2025? Increasingly digital-savvy consumers expect faster decisions, more personalized experiences, and seamless interactions that align with their experiences with other service providers.
Lenders will ultimately need to decide whether to cling to these ‘traditional’ methods or embrace more efficient, data-driven solutions. The rising customer demand for speed, convenience and personalisation may just be the tipping point.
