B2B Payments / Cross-Border
Illustrative scenario

Cut Nostro Break Investigation From Days to Hours With an AI Agent

For a Head of Correspondent Banking Operations managing 15 or more correspondent relationships, a 3-to-5-day break investigation cycle is more than an operational inconvenience — it's a liquidity risk and a staffing drain. When ops analysts spend their days manually reconciling MT940 statements against FIS PaymentsOne records in spreadsheets, the work is slow by design. An AI agent changes that calculus entirely.

Up and running in ~4 wkFor: Head of Correspondent Banking Operations / VP Transaction Banking
Estimate your payback
~3 mo
Payback period
$560K
Est. savings / year
+$420K
Year-1 net

Rough estimate — change the numbers to match your business. We scope the real figures with you on a call.

The Real Cost of Manual Nostro Reconciliation

Correspondent banking operations teams at mid-tier banks typically run $300K–$700K per year just to keep nostro reconciliation functioning. The core problem isn't complexity — it's volume and repetition. Analysts receive MT940 statements, open FIS PaymentsOne, pull Snowflake queries, cross-reference Bloomberg reference data, and manually classify each break. Across 15 correspondent relationships, this process averages 3–5 days per break before a structured investigation report even lands in the right inbox. During that window, unresolved breaks sit as uncertain positions — and under Basel III liquidity rules, uncertainty in nostro positions is not a minor inconvenience.

How an AI Agent Works the Break Investigation Queue

An AI Labor Company agent is trained directly on your ops analysts' existing investigation workflow — the actual steps they take in FIS PaymentsOne and Snowflake, not a generic template. Once deployed, the agent ingests incoming MT940 and MT950 statements, auto-matches against PaymentsOne transaction records, classifies break types (timing, amount, missing entry, duplicate), and routes structured break reports to the appropriate analyst for review. For the roughly 70% of breaks that follow recognizable resolution patterns, the agent can typically deliver a same-day report. The analyst's role shifts from manual extraction to exception review — they receive a pre-classified, pre-documented investigation file rather than a blank spreadsheet.

What This Is Actually Worth to the Business

The efficiency story is straightforward: investigation time drops from 3–5 days to under one day for most breaks, and the agent can be live and producing results in approximately 4 weeks. But the more important mechanism is risk reduction. Faster break resolution means cleaner nostro positions, which matters directly for liquidity reporting and correspondent relationship health. Teams running this workflow can typically cover the same correspondent network volume without adding headcount — and absorb growth in correspondent relationships without a proportional increase in ops cost. Illustratively, a team handling 200 breaks per month at 4 hours of analyst time per break recovers substantial capacity that can be redirected toward higher-judgment work or expanded network coverage.

Works with
FIS Global PaymentsOneSWIFTSnowflakeMicrosoft ExcelSalesforce Financial Services CloudBloomberg
Questions

Does the agent require changes to how FIS PaymentsOne or SWIFT connectivity is configured?

No. The agent is built on top of your existing data connections — it reads from FIS PaymentsOne and Snowflake using current access patterns and ingests MT940 files through your existing SWIFT infrastructure. No core banking reconfigurations are required.

What happens with breaks the agent can't classify?

The agent routes unclassified or ambiguous breaks directly to an ops analyst with all available matched data pre-populated. No break is silently dropped — the agent's job is to reduce manual work, not to make final decisions on complex exceptions.

How long does it take to go live?

Typically around 4 weeks from workflow mining to first production reports. The agent is trained on your team's actual investigation patterns, so the ramp includes a calibration phase before full deployment.

Related use cases

Illustrative scenario for financial services, banking & insurance. Figures are example ranges, not guarantees — we scope real numbers with you on a call.

Want this running in your business?

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