Auto-resolve Reconciliations to Accelerate Month-end Close
AI can match, classify and propose resolutions for transaction and balance mismatches, automatically resolving low-risk items and surfacing high-confidence actions for reviewers; the payoff is fewer manual investigations, faster month-end close and lower operational risk.
Illustrative example only. Every workflow requires its own operational, quality, and risk review.
Before: the work today
Large financial firms ingest millions of transactions across payments, custodial positions, trading ledgers and clearing files; mismatches create a growing backlog of reconciliation exceptions and suspense items. Manual triage is time-consuming, inconsistent, and delays close cycles, increasing cost and audit exposure.
Change: a better workflow
Integrate machine learning, NLP and deterministic rules into the reconciliation workflow so the system handles straightforward matches and prepares evidence-backed recommendations for human reviewers. The pipeline emphasizes explainability, auditable decisions, and a confidence-driven split between auto-posting and escalation to specialists.
- Use supervised and semi-supervised ML models plus fuzzy matching and graph linkage to align transactions across payment rails, custodial statements, trade blotters and the GL.
- Apply NLP to extract payer/payee, invoice refs and remittance info from free-text fields and attachments to boost match rates.
- Implement confidence scoring: auto-post or auto-clear items above a high threshold, route mid-confidence items to a human-in-the-loop review queue, and log low-confidence items for specialist investigation.
- Add RPA connectors to ERP/GL systems for automated postings and create an immutable audit trail with decision metadata, data lineage and versioned models for regulator-ready governance.
- Monitor model drift, run periodic backtests, and incorporate reviewer feedback as labeled training data for continual improvement.
After: illustrative capacity created
Teams typically see 40-70% reduction in manual reconciliation effort and a 30-60% drop in open suspense items, enabling month-end close to shorten by 1-3 days for many firms. Operational cost savings and risk reduction scale with volume; a mid-market to enterprise organization can often reach payback within 6-12 months once data integration and governance controls are in place.
This is an illustrative use case designed to show where better workflows, automation, and AI can create capacity. It is not a description of a specific client engagement. Results depend on your data, processes, and goals.
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