Detect and prevent financial aid fraud in higher education
Build a workflow that detects likely "ghost students" — fake or bot-driven enrollments created to steal financial aid and occupy class seats — and warns staff before the census/add-drop deadline so fraudulent enrollments can be dropped while aid clawbacks and lost seats are still preventable. Trigger it on a daily schedule (cron) during the enrollment window, and also expose a manual/route trigger so staff can run it on demand. At a high level, the workflow should: Pull current enrollment and learning-activity data — student roster, LMS logins, assignment submissions, last-access dates, sign-up timestamps, and email/IP metadata. Source this from your LMS (I used a Canvas-style export — or use Blackboard, Moodle, or a CSV/database drop from your SIS). Score each enrollee against ghost-student signals: never logged into the LMS, zero submissions by the census date, duplicate or patterned emails/IPs, batch sign-up timestamps, and enrollment across many high-aid online sections at once. Produce a ranked "likely ghost" list per course plus an at-a-glance stats view — fraud rate, seats reclaimable, and financial aid at risk. Notify faculty and the registrar with a summary before the census deadline (I posted to Slack and sent email — or use your team's chat tool and mail provider), and serve a dashboard page staff can open to review the flagged list. Keep detection logic transparent and tunable so staff can adjust thresholds, and make sure the review output distinguishes strong signals from weak ones rather than auto-dropping anyone. Tools used: Canvas LMS API, Slack, email
What this prompt builds
This workflow identifies 'ghost students' in higher education institutions, particularly community colleges, who fraudulently enroll to steal financial aid and occupy class seats. It leverages data from LMS and enrollment systems to detect fraudulent behaviors, providing faculty and administration with actionable insights before the census deadline. This helps reduce financial losses, prevent aid clawbacks, and free up class spots for legitimate students.
The problem
Community colleges face significant challenges with ghost students enrolling to fraudulently access financial aid, leading to occupied class seats and financial losses. This workflow detects these fraud patterns by analyzing enrollment and activity data from LMS and other sources, allowing institutions to identify and drop likely frauds early in the enrollment process. By doing so, it ensures that financial aid is protected, classes are accessible to real students, and administrative burdens are reduced for faculty and staff.
Solution and impact
The workflow automates the detection of fraudulent enrollments, saving faculty time and reducing administrative workload. It flags likely ghost students before the census deadline, protecting financial aid resources from fraud and freeing up seats for legitimate students. The solution benefits educators, administrative staff, and real students by improving enrollment accuracy and financial oversight.