AI Reconciliation Engine — Interbank Settlement Operations
Situation & Challenge
A commercial bank processing high daily transaction volumes across four core banking systems was experiencing a persistent mismatch rate requiring large analyst teams to resolve manually each day. The legacy rules-based tool could not learn from past resolutions and generated high volumes of false positives that consumed analyst time without producing fixes.
What Coadjutant Built
A four-layer pipeline: format normalisation across heterogeneous CBS schemas → ML classification of mismatch typology across 26 defined categories → root-cause isolation → automated correction entry generation with human-override at each threshold. Trained on seven years of historical resolution data and retrained weekly on live patterns. Full audit trail on every automated action.