Combating Ghost Students and Financial Aid Fraud in Higher Education: A Proactive Approach

The hidden cost of phantom enrollments

Ghost students are entirely fictitious identities created with stolen or synthetic data, real students who enroll but do not attend, or individuals involved in collusive fraud rings. These schemes target federal and state financial aid programs such as Pell Grants, which are intended for low-income students. The impacts of such fraud are multifaceted, including:

• Financial loss: Institutions face direct losses from improper disbursements, with potential penalties for non-compliance ranging from $500,000 to over $10 million per institution.^[3]
• Compliance risk: Failure to detect fraud can lead to adverse findings in audits by the U.S. Department of Education’s Office of Inspector General (OIG), risking loss of federal aid eligibility.^[4]
• Reputational damage: Publicized fraud cases erode trust among donors, stakeholders and prospective students, impacting enrollment and funding.
• Operational disruption: Faculty and staff spend significant time managing enrollments and aid for non-existent or non-participating students, diverting resources from educational priorities.

The following table summarizes the estimated impacts of ghost student fraud:

Mapping the risk landscape

To mitigate the risks associated with financial aid fraud, institutions must understand the risk landscape and common patterns of fraudulent activity, which include but are not limited to:

• Unusually high dropout or withdrawal rates immediately after aid disbursement
• Multiple enrollments linked to the same IP address or device
• Students with no learning management system (LMS) logins or classroom activity in the first two weeks
• Free Application for Federal Student Aid (FAFSA) inconsistencies, such as conflicting income and household data
• Enrollment surges in low-cost, high-aid-eligible online programs

These patterns are often facilitated by the ease of online enrollment and relaxed verification processes. The challenge is compounded by sophisticated fraud rings using advanced technologies such as AI-generated identities to evade detection.

Strategic Solutions

To address financial aid fraud, institutions should adopt a multi-layered, proactive approach. The following strategies, informed by industry-leading practices, can potentially reduce exposure. While each strategy can have impact, they should be implemented as a series of actions.

• Digital identity verification: Implement robust identity verification at enrollment using biometric tools (e.g., facial recognition, document scanning via services) and multifactor authentication linked to verified phone numbers or addresses. This helps provide a reasonable assurance that only legitimate students receive aid.
• Attendance and engagement validation: Require early course check-ins (e.g., LMS logins and academic engagement within the first seven days) to confirm student participation. AI-based pattern recognition can flag anomalies in class activity, assignment uploads or test-taking behaviors.
• IP and device tracking: Use network analysis tools to detect clusters of applications or class access from the same device, IP address, unusual geographies, or proxy/VPN services, which are indicative of fraud rings.
• Cross-functional fraud response unit: Establish a dedicated team led by a senior official such as the VP of Finance or Chief Compliance Officer, along with members from IT security, financial aid, registrar, audit/compliance, legal and student affairs. This unit centralizes tip reporting, leads investigations and interfaces with the OIG when necessary. Case management tools can streamline tracking and resolution.
• Enhanced policies and controls: Implement automatic holds on aid disbursements until verification and participation milestones are met. Conduct risk-prioritized audits of aid recipients, particularly in online programs, and define clear sanctions for fraud, including aid suspension and federal reporting.
• Advanced analytics and machine learning: Deploy analytics and data visualization platforms like Tableau, Alteryx or Azure ML to build models that flag anomalous FAFSA information, historical drop/enroll cycles, and students with no campus engagement. For example, some schools have used a machine-learning solution to identify 96% of fraudulent applications, preventing nearly $172,000 in losses in a single year.^[5]
• Building a culture of integrity: Promote a culture of integrity through mandatory financial-aid ethics modules for students, fraud awareness training for staff, and anonymous reporting tools with whistleblower protections. Collaboration with other institutions and agencies like the OIG, Federal Student Aid (FSA), the National Association of College and University Business Officers (NACUBO), and the National Association of Student Financial Aid Administrators (NASFAA) enhances fraud detection and prevention.^[6]

Implementation Roadmap

A 12-month roadmap can help institutions roll out systematic implementation of the above solutions:

• Phase 1 (Months 1–3): Assessment and Design - Conduct risk mapping and stakeholder workshops, and select appropriate tools
• Phase 2 (Months 4–6): Infrastructure Build - Implement identity-verification systems, data pipelines and alert mechanisms
• Phase 3 (Months 7–9): Response Activation - Launch the fraud response unit and test incident response playbooks
• Phase 4 (Months 10–12): Optimization - Train machine-learning models, refine detection rules and engage external auditors

Measuring success is key

Success can be measured through the following non-exhaustive KPI metrics:

• Percentage of students verified via biometric/ID tools
• Number of fraud incidents detected proactively versus reactively
• Amount of aid recovered or prevented from improper disbursement
• Time-to-resolution of flagged cases
• Federal audit clearance with no findings

Financial aid fraud, particularly that caused by ghost students, is a critical challenge that demands immediate action. No longer isolated anomalies, these ghost enrollments represent a material threat to institutional integrity, financial stability and brand reputation, as they divert millions of dollars in financial aid, distorting performance metrics and eroding trust among stakeholders. By adopting a proactive, risk-based approach that integrates advanced technologies, cross-functional collaboration and robust policies, institutions can protect their financial resources, ensure compliance and maintain stakeholder trust.