From 3 Days to 15 Minutes: Marshall’s AI-Driven Cash Forecasting

Matt Tidd – Senior Vice President of Finance/CFO, Marshall University

 

Marshall University cut cash-forecasting cycle time from 3 days to 15 minutes by moving from manual, Excel-driven workflows to AI-powered forecasting. In this session, Marshall’s CFO and a practitioner partner unpack the journey: integrating multi-year financial, operational, and external data; modeling tuition cycles, population trends, and cash-spike events; automating what-if scenarios; and using generative AI explanations to build trust with stakeholders.

Attendees will leave with a practical playbook to modernize forecasting for speed, accuracy, and transparency—without ripping and replacing legacy ERP, student, or finance systems.

Marshall University’s treasury team faced timing misses and reconciliation errors in a historically driven, Excel-based cash-forecasting process. Partnering with an AI-enabled forecasting platform, they implemented a modular forecasting engine that ingests finance, operational, and macro-event data, then surfaces predictions with narrative explanations leaders can trust.

The following are the issues addressed and how they were identified:

  • Manual prep created delays and error risk (vlookups, timing lags).
  • Forecasts under-weighted tuition cycles, inflation, and one-time “cash spike” events.
  • Stakeholders lacked transparent rationale behind projections.

Solution approach

  • Data integration: Multi-year GL/treasury data + enrollment/operational feeds + external indicators (inflation, population, tuition/fee events).
  • Feature & event modeling: Tuition bill dates, enrollment shifts, seasonal utilities, and known cash spikes.
  • Scenario automation: One-click what-ifs for tuition changes, enrollment mix, inflation, and payment timing.
  • Explainability: Generative AI creates plain-language rationales for forecast drivers to improve adoption.
  • Delivery: Outputs to dashboards, reports, cube views, or Excel—meeting stakeholders where they work.

Challenges & lessons learned

  • Mapping legacy fields and reconciling historical idiosyncrasies took time—establish a data dictionary early.
  • Start with cash drivers you can explain; add complex signals later.
  • Governance matters: Freeze feature definitions between cycles to reduce drift.

Change management strategy

  • Pilot on one tuition cycle → prove accuracy → expand to full year and other cash drivers.
  • Publish confidence intervals and side-by-side comparisons with prior method for 2–3 cycles.
  • Train finance staff to shift from compilation to analysis.

Learning Objectives

  • Map legacy ERP/student/financial data to a minimal AI feature set for cash forecasting and define quality checks.
  • Run automated what-if scenarios (tuition, enrollment, inflation, timing) and interpret model explanations with stakeholders.
  • Apply a phased change-management plan to expand from cash forecasting to adjacent use cases (enrollment, fundraising, utilities).

CPE Available

  • 1 Credit: Information Technology