Income and employment verification fraud is surging in the tenant screening industry, putting traditional verification methods under intense pressure. As economic uncertainty grows and document forgery becomes more sophisticated, it’s clear that legacy processes are no longer sufficient. Recent findings highlight the urgency for change.
According to the NMHC Pulse Survey, 93.3% of property managers reported encountering fraud in the past year, with 84.3% citing falsified paystubs and fake employment references as the most common tactics. As AI-generated forgeries become increasingly convincing and accessible, relying solely on manual review is proving inadequate.
A Shift in Strategy: Toward Smarter Income and Employment Verification
Historically, tenant screeners have relied on methods such as manual document review, direct employer contact, payroll APIs, and verification of assets (VOA). While these remain important, they are no longer capable of keeping pace with today’s verification challenges.
In response, many screening companies are exploring new income verification tools that offer improved fraud prevention, lower operational costs, and faster turnaround. These innovations include layered approaches that combine observed data, permissioned uploads, and automated fraud detection technologies.
Introducing Observed Data in Tenant Screening
One emerging solution in the fight against rental application fraud is the use of observed data during tenant screening. This method uses [KA1] collectively sourced insights to assess whether an applicant’s income and employment claims are likely to be accurate.
Observed data is drawn from a consortium of financial institutions, lenders, and dealerships. It includes a confidence grade based on actual financial behavior, such as account activity and application history, which are then compiled and analyzed to form a current view of income and employment patterns. [CC2] These insights are drawn from the latest self-reported data submitted by consumers through loan applications, providing screening companies with a dynamic, data-driven benchmark for verification.
Although this method is not FCRA-compliant and cannot be used to approve or deny applications, it is highly effective as an early step in the screening process. A confidence score is often included to help screeners assess how closely an applicant’s stated information aligns with observed trends and can help screening companies to better assess their prioritization queue to determine if more data points are needed.
Why Observed Data Matters
To combat fraud without driving up costs or slowing down the tenant screening process, screening companies need reliable, efficient tools. Observed data supports this need by offering a faster, more scalable approach to assessing risk.
Key benefits include:
- Early detection of discrepancies in reported income or employment
- The ability to prioritize high-risk applications for further review
- A more cost-effective alternative before committing to premium verification services
For instance, if an applicant has a strong credit report and clean background check, and observed data supports their stated income, further verification may be unnecessary. If inconsistencies are flagged, screening companies can escalate to tools such as AI document analysis or direct outreach.
Fraud Prevention Through Smarter Workflows
The use of observed data also aligns with a broader shift toward AI document fraud detection and layered verification strategies. Instead of applying the same tools to every application, screening companies can now implement decision trees that use lower-cost tools first, escalating only when risk or uncertainty increases.
This adaptive approach is particularly relevant as screener companies strive to improve accuracy and efficiency at scale. By deploying observed data as a first step, tenant screening professionals can better allocate resources while remaining vigilant against fraud
Future Proofing Verificaiton
As the income and employment verification landscape evolves, screening companies must move beyond legacy methods and adopt tools that are responsive to today’s challenges. Observed data provides a scalable, low friction starting point that supports smarter decision-making and better fraud detection.
Coming to our next blog: We will explore how manual research verifications and AI-powered document upload solutions enhance the effectiveness of modern income verification tools, creating a more resilient and adaptable tenant screening process.
