Case study · Higher education — finance operations · Singapore · Singapore Management University
The work before automation
The challenge
Every cycle, staff moved reports from UOB, SAP, the Integrated Student Information System (ISIS) and Cybersource into reconciliation Excel files, then worked through each CVENTS, TMS and ISIS file — checking that daily and bank amounts tallied, determining refund rows, pivoting ePayment and SF lines, and running the reconciliation logic. A single ISIS file took around two hours; CVENTS and TMS files took thirty minutes each.
Before
What the team had to do
Move reports from UOB, SAP, ISIS and Cybersource into the recon Excel files
For each CVENTS file: check daily and bank amounts tally, determine refund rows, run recon logic (~30 min)
For each TMS file: check tallies and run recon logic across Cyber, FBL3N, Daily, Collection and JE sheets (~30 min)
For each ISIS file: check tallies, pivot ePayment and SF lines for data hashing, flag discrepancies (~120 min)
With the robot
What now happens automatically
Runs the same checks on every CVENTS, TMS and ISIS file — about one minute per file
Applies the reconciliation logic and reporting exactly as documented
Marks discrepancies and refund rows for a person to review
Leaves every decision on flagged items to the finance team
Built around the existing process
Systems involved
- Microsoft Excel
- SAP
- UOB bank reports
- ISIS
- Cybersource
Verified results
90% faster
Overall reconciliation effort
Measured: Before/after timings recorded by the SMU Office of Finance (Systems & Transformation) in the public process overview, June 2024.
180 min → 3 min
Data processing per cycle
Measured: Per-file run times measured by the SMU Office of Finance before and after RPA implementation.
175 min saved / month
Staff time returned to the team
Measured: Documented in the SMU public process overview, June 2024.
Process documentation
