Higher education institutions are facing a new generation of fraud threats. While cyberattacks, data breaches, and financial scams have long challenged universities, synthetic identity fraud is rapidly emerging as one of the most costly and difficult-to-detect risks.
Fueled by AI and increasingly sophisticated fraud networks, such schemes were responsible for $150 million sent to ineligible students in 2025, including $30 million to people who were dead and had their identities stolen.
Synthetic identity fraud is when criminals combine legitimate personal information with fabricated data to create new identities that appear authentic. Sometimes, fraudsters use entirely fictional identities supported by AI-generated documents, images, and application materials. The goal is the same: to deceive victims and steal money.
How synthetic identity fraud is hitting higher education
Higher education has become an attractive target because institutions process large volumes of applications and digitally distribute significant amounts of financial aid and student funding.
“Because the attempts are getting so much more sophisticated, both our staff and our students are very vulnerable to how good these fakes are,” says Janet Burkhardt, Assistant Vice Chancellor for University Financial Services at the University of Denver, in a recent Converge podcast.
For educational institutions managing millions of dollars in student payments, scholarships, grants, and cross-border transactions, the challenge extends far beyond enrolment fraud. Synthetic identities can create vulnerabilities throughout the payment life cycle, increasing financial losses, operational burdens, and compliance risks.
Ghost students: What they are
Ghost students are fraudulent applicants who use manipulated identities — or who don’t exist at all.
Typically, fraudsters create or obtain synthetic identities and use them to apply for admission and financial aid. Once enrolled, ghost students remain active just long enough to receive funding before disappearing from the educational institution.
Because these criminals often complete enough administrative steps to appear legitimate, institutions may not detect the fraud until after funds have been disbursed.
“They can use AI tools to do their assignments, and then they get grades … and so they look very, very legitimate,” Burkhardt says.
The result is a costly cycle of investigations, recovery efforts, and compliance reporting.
Where universities are most vulnerable — inbound and outbound
Universities face synthetic identity fraud risks at both ends of the payment process.
Inbound payments create one set of vulnerabilities. Fraudsters may use stolen payment credentials, compromised accounts, or manipulated identities during application, enrollment, and tuition payment processes. In some cases, bad actors exploit weaknesses in identity verification systems to establish credibility before attempting more sophisticated financial schemes.
Outbound payments often present even greater risk. Financial aid disbursements, scholarship payments, tuition refunds, and other student payments are attractive targets because they combine large transaction volumes with tight processing timelines.
Warning signs may include:
- Multiple student records linked to a single bank account
- Frequent changes to payment instructions
- New bank accounts added shortly before disbursement
- Unusual refund requests
- Multiple applications originating from the same device, IP address, or geographic location
Without adequate monitoring and verification controls, fraudulent payments can move through institutional systems before university staff recognize suspicious activity.
How AI phishing attacks exploit institutional trust
Universities are particularly vulnerable because they rely on trust-based communication across large, decentralized populations of students, faculty, staff, and external partners — and AI is making fraud schemes more convincing than ever.
In the past, traditional phishing attacks often contained obvious warning signs, such as poor grammar, suspicious formatting, or generic messaging. Today’s AI-powered attacks are significantly more sophisticated and personalized.
Fraudsters can use generative AI to create highly personalized communications that mimic students, faculty members, administrators, financial aid representatives, or trusted vendors. These messages may reference real institutional processes, deadlines, or payment requests, making them even more difficult to distinguish from legitimate correspondence. They may request sensitive student information or direct approval of payment.
Once attackers gain access to institutional systems or payment workflows, the resulting financial losses can be significant.
What robust payment fraud detection looks like in practice
Leading educational institutions are adopting multilayered approaches that combine identity verification, payment monitoring, and risk-based controls throughout the student lifecycle.
Key components include modern identity verification practices such as authenticating government-issued identification, performing biometric verification, and comparing applicant and bank account information against trusted data sources.
Continuous monitoring helps identify unusual payment behavior, suspicious account activity, and anomalies that may indicate fraud. Machine-learning models can strengthen these efforts by analyzing behavioral patterns, device information, transaction history, and identity signals to flag high-risk activities for additional review.
Segregation of duties, dual approvals, and exception management procedures can help prevent unauthorized payment changes from moving through institutional systems unchecked. Together, these measures create multiple opportunities to identify fraud before funds are released.
Staff training and verification discipline as a first line of defense
Technology alone cannot eliminate fraud risk. Higher education employees remain one of the most important lines of defense against payment fraud and social engineering attacks.
Universities should establish clear procedures for verifying identity, confirming payment instructions, and escalating suspicious activity. Staff should be trained to recognize phishing attempts, synthetic identity indicators, and common fraud tactics targeting higher education institutions.
After all, many fraud incidents succeed because attackers exploit urgency and trust. Strong human verification discipline helps counter both.
How a payment partner strengthens institutional fraud resilience
As synthetic fraud becomes more sophisticated, universities increasingly recognize that payment security is not solely an internal responsibility.
The right payment partner, such as Convera, can provide an additional layer of protection by supporting secure payment workflows, account validation, transaction monitoring, and fraud detection capabilities.
A specialized payment provider may help institutions:
- Verify the recipient’s banking information
- Reduce payment errors
- Identify suspicious transaction patterns
- Strengthen payment authentication controls
- Improve visibility across domestic and international payment activity
- Support compliance and audit requirements
For educational institutions managing global payment flows, international tuition payments, and cross-border disbursements, these capabilities can be especially valuable.
As fraud tactics continue to evolve, universities must strengthen both their payment controls and their ability to detect suspicious activity before funds are disbursed.
By combining strong verification practices, trained staff, intelligent fraud detection tools, and trusted payment partners, institutions can better protect student funds, safeguard institutional resources, and strengthen resilience against one of higher education’s fastest-growing financial threats.
