AI Integration with Ellucian Banner Tuition Calculation

AI integration connects directly to Banner's core financial data objects and calculation engines. The primary surfaces are the Student Accounts Receivable (SAR) module for billing, the Financial Aid (RORAID, RORBASE) modules for packaging, and the residency determination workflows often managed through General Person (GENERAL) or custom forms. An AI agent acts as a policy interpreter, ingesting rules from institutional catalogs, state guidelines, and program-specific fee schedules stored in tables like STVCHRT, STVTERM, and STVFEE. By querying student records (SGBSTDN for term data, SFRSTCR for course registration, SHRDGMR for degree intent), the system can pre-calculate charges, flag discrepancies, and generate human-readable explanations for any non-standard assessment.

Implementation typically involves a middleware layer that subscribes to Banner events—such as a student's course registration (SFRSTCR insert), program change (SGBSTDN update), or residency status update. When triggered, the AI agent calls the relevant Banner API or direct database view to gather context, then uses a fine-tuned LLM or rules engine to apply the institutional policy. High-value workflows include: automated residency reclassification review by analyzing documents linked in Banner Document Management (BDM); per-credit fee calculation for part-time, overload, or differential tuition programs; and late-add or drop refund proration that considers term calendars and payment history. The output is a structured payload recommending adjustments to TBRACCD (student account detail) or RORAID (financial aid) records, which is then routed through Banner's existing approval workflows (GURPRLE) for an auditor's final review and posting.

Rollout requires a phased approach, starting with a single, complex fee policy (e.g., lab fees for science courses) to build trust in the AI's logic. Governance is critical: all AI-generated calculations must be logged in a dedicated audit table (GURAIAL), retaining the prompt, source data, and reasoning chain. A human-in-the-loop checkpoint is maintained for all transactions over a defined dollar threshold or for students in exceptional statuses (e.g., international, veterans). This architecture doesn't replace Banner's core calculation engines but augments them, handling the "edge cases" that often consume 80% of staff time, turning manual review processes from hours to minutes while maintaining a complete audit trail for compliance.

The integration connects to Banner's core financial tables—TBBINST (Installment Plans), TBRACCD (Account Codes), and TVRESC (Residency Rules)—via Banner's SOAP or RESTful APIs (e.g., Banner General or Banner 9 APIs). An AI orchestration layer acts as a middleware service, listening for events like a student's course registration (SFAREGS), residency status change, or term activation. It ingests the relevant student record, fee schedule, and policy documents, then calls an LLM (like GPT-4 or Claude) via a secure, dedicated endpoint to interpret the applicable rules and calculate adjustments or exceptions. The results—a proposed tuition amount, fee waiver, or residency classification—are passed back to a human-in-the-loop approval queue within a custom dashboard or directly into Banner's workflow engine (GJAPCTL) for a bursar officer's review before final posting to the student account (TBRACCD).

Critical guardrails are implemented at multiple levels: Data masking strips sensitive PII (like SSN) from payloads sent to the LLM. A rule validation layer cross-checks the AI's output against hard-coded Banner business logic (e.g., maximum credit hours for flat-rate tuition) to prevent policy violations. All transactions are logged to a dedicated audit table with the original input, AI reasoning chain, final decision, and approving officer's ID for compliance (FERPA, PCI). The system is designed for idempotency, ensuring recalculations triggered by data changes don't create duplicate charges. For rollout, we recommend a phased approach: start with a single fee category (e.g., lab fees) for a pilot student cohort, monitor accuracy rates, and gradually expand to full tuition and residency calculations.

This architecture ensures the AI augments—rather than replaces—the existing financial aid and bursar office workflows. It reduces manual review from hours to minutes for complex cases like out-of-state residency appeals or study abroad fee packages, while maintaining the necessary human oversight and full traceability back to Banner's system of record. For a broader view of integrating AI across Banner's modules, see our guide on AI Integration for Ellucian Banner.

A production integration must treat the AI as a decision-support layer, not a black-box replacement for Banner's core STVTERM, SGBSTDN, and SFRRCRF logic. The typical architecture involves a middleware service that intercepts calculation requests, uses an LLM to interpret policy documents and exception narratives, and then returns structured recommendations (e.g., adjusted fee codes, residency flags, waiver amounts) to Banner's batch or real-time processes. All AI inputs (student records, policy text) and outputs (recommendations, confidence scores) are logged to a dedicated audit table with trace IDs linked back to the original Banner transaction, ensuring a complete lineage for financial aid audits and bursar reconciliations.

Security is enforced through role-based access controls (RBAC) at the integration layer, ensuring only authorized financial aid officers or bursar staff can trigger or approve AI-suggested overrides. Personally Identifiable Information (PII) and financial data are never sent directly to a third-party LLM; instead, a local embedding model or a carefully scrubbed prompt using internal codes is used for policy interpretation. The system is designed for human-in-the-loop approval, where any AI-proposed deviation from standard calculation must be reviewed and confirmed by a staff member within the Banner interface before being committed, maintaining fiduciary responsibility.

Rollout follows a phased, risk-gated approach: Phase 1 targets non-resident tuition and complex fee scenarios for a single academic term, allowing the financial aid office to validate AI accuracy against manual reviews. Phase 2 expands to residency reclassification appeals and study abroad billing, incorporating feedback into the prompt library. Phase 3 enables proactive scenario modeling for financial aid packaging, where advisors can use a copilot to simulate policy changes on a student's cost of attendance. Each phase includes parallel run comparisons and defined rollback procedures to Banner's native calculations, ensuring operational continuity and building institutional trust in the AI-assisted workflow.