Financial institutions deploying AI expense auditing typically target three numbers: fewer analyst hours consumed by routine review, faster reimbursement cycles, and a lower operational loss ratio from policy violations that used to slip through. Each is measured against your own baseline, which we document in week one. The mechanisms are direct: policy validation and coding checks run on every transaction instead of a sample, so analysts review exceptions rather than files; vendor screening runs consistently against your watchlists, so duplicate vendors, shell-entity patterns, and high-risk geographies get flagged the day they appear instead of at the next audit.
Run the stakes math on your own ledger: pull last quarter's expense volume, your exception rate, and the hours your team logged clearing that queue - that is the recurring cost this system exists to remove. Over 12 months the return compounds: monthly recalibration cuts false positives as your team's dispositions teach the model your institution's risk tolerance, and SOX 404 attestation prep gets cheaper because exception reports map to named control objectives as decisions happen instead of being reconstructed for examiners. Model it on your own volumes and staffing before you believe any vendor's ROI percentage - including ours; that math only runs on your own ledger. The free AI Opportunity Assessment is where that conversation starts: a directional read on where the auditing opportunity is biggest across your institution, plus a phased roadmap - not a volume/staffing model built for you.