Traditional approaches to income verification have involved querying large payroll databases that can create coverage gaps for both non-standard workers and the “long tail” of W-2 employees. It is time for a change.
Closing the Gaps: Self-Service Drives Efficiencies and Inclusion
Watch this don’t miss webinar with SteadyIQ and speakers such as Melissa D. Wolf, Family Support Division Deputy Director, State of Missouri, as they share current agency income verification challenges, trends, and introduce innovative, people-focused income verification technology that delivers real-world results for SNAP, HHS, Medicaid/UI agencies. Speakers discuss how transforming verifying income is a crucial step for means-tested public benefits and can greatly increase efficiency and coverage.
In the webinar, speakers share insights on new, innovative technology—Income Passport™ Powered by SteadyIQ—that is “user-permissioned” technology that addresses this issue, while also putting the power of data back into the user’s hands. Applicants can permission access, create an income report, and consent to it before it is sent back to the state for a determination through a SteadyIQ proprietary process that empowers benefit applicants to connect to the sources (bank accounts, payroll accounts, digital wallets, etc.) where they earn income.
In this way, the SteadyIQ user-permissioned technology creates transparency and trust between government agencies and applicants, while also driving efficiency and ensuring accuracy for the programs themselves.
Watch this LexisNexis® Risk Solutions and SteadyIQ co-hosted webinar to learn insights on the future of income verification technology and the changing world of work.
Get a clear view of your program’s participants to pinpoint fraudsters and stop improper payments.
Ensure equitable access and smooth service delivery with a precise, whole-person view
Prevent and detect dual participation and cross-program participation that results in the improper disbursement of benefits to public assistance program participants.