Is Your Financial Aid Strategy Built for Today? Rethinking Financial Aid in Higher Education

As every institutional leader knows too well, there is now unprecedented pressure facing those tasked with managing financial aid in higher education.

In a recent Liaison webinar, Is Your Financial Aid Strategy Built for Today? Laying the Foundation for Prescriptive, AI-Driven Optimization, Hayley Wolf and Craig Cornell, both vice presidents at Liaison and long-time enrollment leaders, outlined why traditional financial aid strategies are no longer sufficient. Instead, predictive and prescriptive analytics must now modernize aid to better support enrollment, revenue, and student success.

Historically, financial aid awarding was designed around compliance and standardized formulas. As Wolf summarized, the traditional approach typically centers on:

• Merit and need-based focus | GPA, test scores, Student Aid Index.
• Static allocation | A leveraging model set early in the cycle and rarely changed.
• Standard distribution | Even, term-based disbursements.
• Compliance-driven orientation | Department of Education regulations as a primary lens through which aid decisions are made.

These models helped build decades of practice, but they were never designed for the volatility institutions now face. Cornell emphasized how the role of aid has expanded:

“It’s no longer just being reactive. It’s no longer just being compliant with Department of Education… Now, how do we leverage financial aid appropriately on top of being compliant?”

The result is a mismatch between rigid models and dynamic realities that is shaped by more price-sensitive families, fewer applicants in some segments, and heightened scrutiny from boards, legislators, and the public.

Both presenters stressed that standing still carries real risk for financial aid in higher education. Cornell outlined several consequences of relying on “set it and forget it” aid models:

• Misaligned strategies that don’t reflect current demand patterns or behaviors.
• Small missteps in allocation that quietly erode net tuition revenue (NTR) over time.
• Inability to forecast the impact of changes until it’s too late in the cycle.
• Underutilized funds, especially in endowments and foundations.

He shared a real-world example from an institution he worked with:

“They didn’t award all the aid they had every year because they didn’t know how to award it properly to the right students. How many students did they lose because they didn’t spend that money?”

For financial aid leaders, this is not just a missed opportunity—it’s a governance and stewardship concern.

Wolf pushed back on the idea that modernization means discarding everything that has worked.

“We don’t need to get rid of everything we’ve always done,” she said. “But I think there’s a lot of enhancements that have been created and developed that we can now leverage to be more agile and nimble.”

Wolf contrasted traditional awarding with a modern, analytics-enhanced approach that spans several dimensions, including:

1: Data Use

• Traditional: Historical data and broad trends.
• Modern: Advanced analytics and predictive modeling for real-time insight.

2: Aid Awarding

• Traditional: Standardized, banded awards (e.g., “3.5 GPA gets $10,000”).
• Modern: Personalized, student-level sensitivity and “tipping point” analysis.

3: Goals

• Traditional: Costs covered to attract students.
• Modern: Optimized for enrollment yield, NTR, academic quality, and mission fit.

4: Process

• Traditional: Manual, reactive, prone to bottlenecks.
• Modern: Proactive, technology-augmented, continuously recalibrated.

Of course, leveraging and compliance are still the foundation. But optimization layers new capabilities on top to align aid more tightly with institutional strategy.

One of the central shifts is moving from “average need” to individual price sensitivity. Two students with identical academic profiles and similar FAFSA data may have very different thresholds at which they can—or will—enroll.

Modern optimization tools model this at the individual level, asking:

• How likely is this specific student to enroll under their current offer?
• What is the predicted change in likelihood if we adjust institutional aid?
• Where is the marginal “tipping point” beyond which additional dollars don’t materially change behavior?

This allows financial aid leaders to focus scarce institutional funds where they have the greatest incremental impact on enrollment, persistence, and revenue, instead of spreading dollars evenly in the name of perceived fairness.

A second major evolution for financial aid in higher education is the integration of aid strategy into retention and persistence, not just recruitment.

Wolf noted that many institutions still treat aid as a front-loaded recruitment lever. For example:

• Awards are set at admission and remain largely static.
• Retention interventions are often reactive, triggered after a student signals distress or fails to register.

By contrast, predictive and prescriptive analytics can:

• Flag students at high risk of stopping out due to financial stress
• Use multi-source data (engagement, academic behavior, card swipes, geography, etc.) to identify and reduce risk.
• Support proactive micro-interventions, including targeted aid adjustments or non-monetary supports.

As Cornell pointed out, state performance funding models and institutional strategies are increasingly focused on progression and completion, not just freshman headcount. Aid cannot be optimized for enrollment alone; it must be aligned with long-term student success metrics.

When many campus leaders hear “AI,” they think about generative tools and chatbots. Wolf made clear that the most transformative applications for financial aid in higher education involve the use of analytical AI—the predictive and prescriptive engine behind enrollment and aid decisions.

For example, Liaison’s Othot AI platform delivers:

• Predictive analytics | Assigns each student an enrollment or persistence probability; uses institutional historical data, not generic national benchmarks; and forecasts class size and NTR before deposits are due.
• Prescriptive analytics | Simulates “what if” scenarios (e.g., changing scholarships for a segment); recommends concrete actions (aid adjustments or other interventions); and re-forecasts in real time as behavior and data change.

You don’t need perfect data to begin, but you do need clear institutional goals, a basic data infrastructure, and a willingness to pilot, measure, and iterate.

Wolf emphasized that most institutions already have enough data to start; the bigger lift is building a culture that treats financial aid in higher education as a strategic lever to be continually optimized, not a one-time annual exercise.

To hear the conversation, tune in now to Is Your Financial Aid Strategy Built for Today? Laying the Foundation for Prescriptive, AI-Driven Optimization.