Finance operations · Cash application · Reconciliation · North America
Cash-application reconciliation: 30–40 hours a month automated for a regulated finance firm.
Anonymized case study: a regulated finance firm eliminated 30–40 hours per month of manual cash-application and wire reconciliation using an AI system deployed inside their own environment. Zero data left the firm.
Updated 2026-06-21
Original AI-generated editorial artwork.
- Client
- Anonymized finance firm — North America
- Region
- North America
- Focus
- Finance operations · Cash application · Reconciliation
The challenge
A regulated finance firm's operations team spent 30–40 hours every month manually matching incoming payments — wires, ACH transfers, and lockbox deposits — to open receivables. Every payment required pulling remittance data from multiple sources, cross-referencing account numbers, resolving mismatches, and logging the match into the firm's system of record. Exceptions required escalation and manual investigation. The process was entirely human-driven, error-prone under volume spikes, and completely invisible to leadership until month-end. Sensitive payment data meant no external tool or public AI could touch the workflow.
The build
Consultance AI built a cash-application agent deployed inside the firm's own infrastructure. The system ingests incoming payment files and remittance data, runs deterministic matching logic against the open receivables ledger, flags exceptions with a structured exception report, and routes confirmed matches for a single human approval click before posting. High-confidence matches (the large majority) clear automatically. Exceptions are ranked by dollar size and age, so the team's remaining manual time is spent on the items that actually warrant it. Deployed inside the firm's environment — no payment data leaves, no external model sees it.
Evidence from the work
- Production system handling full monthly payment volume for a regulated finance firm — not a demo, not a pilot.
- Matching logic tested against 3 months of historical payment data including clean, messy, and exception cases before go-live.
- Exception report verified accurate: every flagged mismatch had a documented reason and recommended resolution.
- Human approval checkpoint maintained before any match posts to the ledger — firm retains full control over the output.
- Data sovereignty met in full: all payment and remittance data processed inside the firm's own environment.
The outcome
- 30–40 hours of manual matching per month eliminated for the ops team
- Month-end reconciliation completed days earlier — no more end-of-month crunch
- Exception rate visible in real time, not discovered at close
- Zero external data exposure — payment data processed entirely inside the firm's walls
- System owned by the firm: code, matching logic, and prompts transferred at delivery
Recorded impact
Time Saved
40h of 60h
Operator hours reclaimed every month after the system went live.
Efficiency
89% of 100%
Throughput uplift on the workflows we automated end-to-end.
System Score
92 of 100
consultance.ai readiness rating across stack, ops, and observability.
Figures from the existing case-study record; the readiness rating is Consultance’s internal assessment.
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