Secure and Scalable Multi-Agent Orchestration for Enterprise Financial Document Processing

Banking document reviews depend on whether information agrees across a case file. Reading each document correctly is only part of the job. In loan processing, KYC onboarding and trade finance, staff must also reconcile names, amounts, dates and conditions before a case moves forward. OCR and fixed rules work reasonably well on predictable documents, but exceptions and free-text entries still require judgment. This paper describes a multi-agent document-processing architecture that keeps that judgment separate from operational authority. Specialized agents identify documents, extract fields and flag inconsistencies. A rule-driven orchestrator controls case status, permitted tool calls and release to downstream banking systems. Extracted values retain references to their source pages and text locations, while policy rules determine when human review is required. The paper sets out a test plan covering cross-document accuracy, matched-compute baselines, audit evidence, prompt injection and throughput. The architecture is proposed rather than deployed; no measured performance results are claimed.