Pipelines
Cutting discrepancy identification by 90% in statement reconciliation
Automated reconciliation
A language model reads the statements out of emails and PDFs and matches them to the processed files; discrepancies surface at once.
The engagement: existing, as built, in production
Existing
The challenge
In the technical accounting workflow, accountants opened statement-of-account documents from email or PDF by hand, extracted the figures and cross-referenced them against the processed files. Payment cycles waited on it, and mismatches surfaced late.
As built
What we built
- An LLM-powered ingestion pipeline parsing emails and PDFs to extract the statement figures
- Automatic comparison of the extracted amounts against the processed files
- Automated mismatch alerting for immediate correction or re-submission requests
In production
The impact
- Discrepancy identification more than 90% faster
- Cash flow accelerated by removing the manual processing delay
- Operational risk reduced by eliminating copy-and-paste reconciliation
90%+
faster discrepancy identification
The same problem elsewhere
The same matching runs wherever two systems are supposed to agree: supplier invoices against orders, shipments against manifests, meter readings against bills, remittances against claims.
- Insurance & reinsurance
- Statements of account matched against processed files, with the figures read out of PDFs and email.
- Manufacturing & engineering
- Supplier invoices matched to purchase orders and goods receipts, with the mismatch flagged the same day.
- Logistics & transport
- Carrier invoices reconciled against shipments and contracted rates.
- Energy & utilities
- Settlement statements and meter data reconciled against billed amounts.
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info@ainurinsights.com