Transaction Matching Engine
Exact, fuzzy, and ML-predicted matching layers — 92–97% auto-match rates in production.
Six AI capability pillars deployed in production across banking, manufacturing, retail, and SaaS finance functions.
Exact, fuzzy, and ML-predicted matching layers — 92–97% auto-match rates in production.
Auto-classifies exceptions and suggests resolutions for 70–80% of recurring patterns.
SWIFT, ISO 20022, and BAI2 bank feeds matched against your ERP cash book, AR, and AP in real time.
Matches intercompany balances across all entities and generates elimination journals for SAP BPC or Oracle HFM.
Pre-built connectors for SAP, Oracle, NetSuite, Workday, BlackLine, Coupa, and 50+ global banks.
Immutable audit log with full sign-off trail — SOX-compliant close packs generated on demand.
Six AI capability pillars deployed in production across banking, manufacturing, retail, and SaaS finance functions.
Exact, fuzzy, and ML-predicted matching layers — 92–97% auto-match rates in production.
Auto-classifies exceptions and suggests resolutions for 70–80% of recurring patterns.
SWIFT, ISO 20022, and BAI2 bank feeds matched against your ERP cash book, AR, and AP in real time.
Matches intercompany balances across all entities and generates elimination journals for SAP BPC or Oracle HFM.
Pre-built connectors for SAP, Oracle, NetSuite, Workday, BlackLine, Coupa, and 50+ global banks.
Immutable audit log with full sign-off trail — SOX-compliant close packs generated on demand.
That's $500K–$2M+ a year in labour. Our Reconciliation Agent cuts it to under $2 per transaction at 92–97% auto-match.
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Four matching layers — exact, tolerance-adjusted, fuzzy-string, and ML-predicted — achieve 92–97% auto-match rates validated across diverse enterprise environments.
Transactions match continuously as they flow through your bank feeds and ERP, so 85–90% of reconciliation is done before close — compressing cycles from 8–10 days to 1–2.
Every reconciliation action is logged in an immutable audit trail. SOX sign-off enforces segregation of duties, with variance-analysis close packs ready for auditors on demand.
The Autonomous Reconciliation Agent connects to leading ERP, financial close, and treasury platforms.
We build on leading foundation and domain models, evaluated continuously.
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Everything enterprise finance and accounting leaders need to know about deploying an Autonomous Reconciliation Agent at scale.
Contact Our TeamIt matches transactions across bank statements, ERP ledgers, payment processors, and intercompany accounts, then classifies exceptions and enforces SOX-compliant sign-off.
Expect 92–97% auto-match on production volumes. The ML model learns your historical decisions in 4–6 weeks; the unmatched 3–8% are genuine exceptions.
Native connectors for SAP S/4HANA, Oracle Fusion, NetSuite, Workday, BlackLine, and Coupa, plus SWIFT MT940, ISO 20022 camt.053, BAI2, and 50+ bank APIs.
It consolidates intercompany AP/AR from every entity's ERP, flags timing and FX discrepancies, and generates elimination entries plus multilateral netting.
Transactions match within minutes of posting instead of in bulk at close, so 85–90% is done before close — compressing cycles from 8–10 days to 1–2.
Every match, override, and sign-off lands in an immutable audit log with enforced segregation of duties, cutting audit evidence prep by over 85%.
Six to ten weeks: connectivity, parallel run, phased go-live, then ML tuning. You need source-system access, a 3–6 month extract, and a finance sponsor.
Cost per transaction drops from $15–$40 to under $2 — $1.3M–$3.8M a year at 10,000 items per close, with payback within 4–6 months.
Generic financial reconciliation software leans on static rules. Our account reconciliation software adds four matching layers, including ML, for 92–97% auto-match.
Yes. As month end close software it matches continuously so 85–90% is done before close; as balance sheet reconciliation software it auto-generates account packs.