Autonomous Resolution of Cross-System Transaction Mismatches
Match and resolve transaction discrepancies across financial systems with an auditable AI agent.
Problem Context
Financial reconciliation teams spend enormous effort manually matching transactions across payment processors, ERP systems and bank statements. Duplicate payments, partial settlements and missing references create costly exceptions that require expert investigation.
Challenge Requirement
Build an AI agent that ingests transaction data from multiple systems, detects mismatches, retrieves supporting evidence, classifies discrepancy types, recommends resolution actions, and produces an auditable action trail — with human approval for consequential financial updates.
Mandatory Capabilities (8)
Illustrative Scenario
A mid-month reconciliation reveals 47 unmatched transactions between the payment gateway and the ERP. The agent cross-references order IDs, timestamps and amounts, identifies three categories — duplicate retry charges, currency rounding errors and missing settlement references — and proposes automated write-offs for sub-threshold differences while escalating consequential corrections for finance team approval.
Safety & Governance Rules
Consequential financial updates must be gated by human approval. The system must maintain an immutable audit log of all classification decisions and actions taken.
RECOMMENDED EXECUTION TRACE
BUILD REQUIREMENT
Demonstrate an end-to-end reconciliation flow: ingest mismatched transaction data, classify each discrepancy, retrieve evidence, recommend actions, obtain human approval for consequential updates, and produce an auditable action trail.