Stopping Fraud at the Front Door: An AI Screening Model Before SIS Entry

Fraudulent and suspicious admissions applications are creating growing challenges for colleges and universities. These include manual cleanup, data quality issues, and downstream risk once bad records enter core campus systems. Many institutions still address this problem too late, after records have already reached the student information system. This session shares a practical upstream approach to screening suspicious applications before they move into the SIS. The model combines AI-assisted scoring, duplicate identity detection, batch-pattern analysis, and human review to identify records that may require closer examination. Rather than replacing existing admissions workflows, it adds a focused and explainable checkpoint that works alongside current enterprise processes. Attendees will gain a replicable framework for designing an AI-supported screening process that helps reduce manual vetting, improves data integrity, and strengthens operational controls. The session will highlight where institutions can intervene in the data flow, what kinds of fraud indicators to evaluate, how to structure confidence-based review, and how to measure whether the process is improving both efficiency and accuracy over time.