How One College System Tackled Enrollment Fraud with an AI-Driven Identity Verification Strategy

Most admissions teams still operate reactively, manually reviewing applications one at a time and flagging those that appear suspicious. This process is inherently slow and resource-intensive, does not scale, and often misses fraud that does not immediately stand out. Meanwhile, ghost students, stolen identities, and financial aid fraud are no longer outliers. They’re a growing, AI-enabled threat that requires a more adaptive response. This session introduces a risk-based framework used across the Alabama Community College System (ACCS). By layering AI-powered risk signals, institutions can automate clear decisions, focus human review where it matters on legitimate applications, and stop fraud before they reach the student information system (SIS). Verification requirements adjust dynamically based on risk, reducing friction for legitimate students while improving outcomes. The approach extends to AI-enabled population-level analysis, mapping connections across applicants to uncover coordinated fraud patterns invisible to any single college. Presented by enrollment and IT leaders from ACCS alongside their solution partner, this session walks through the full deployment across two dozen colleges. Participants will leave with a proven framework for tiered, risk-based verification, a governance model for standardizing identity procedures across decentralized campuses, and a roadmap for leveraging AI-driven signals to detect and prevent coordinated fraud at scale.