A global financial services provider managing high-volume transactions across fragmented systems faced significant administrative friction in cash allocation, invoice-matching, and financial reporting; making reconciliation more complex and limiting financial teams’ capacity for higher-value analysis.
Automating Accounts Receivables & Multi-Bank Reconciliation with AI
Overview
Business Complexity
Finance teams managing 14 bank accounts and 10 global entities relied heavily on manual data matching and spreadsheet-driven workflows. Limited cash flow visibility and fragmented processes added complexity to reconciliation and reporting. This was an opportunity to use AI agents to automate repetitive finance workflows while improving visibility, validation, and control.
Action
- Engineered a multi-agent architecture to automate complex accounts receivable workflows, including data extraction, bank reconciliation, and audit tracking.
- Deployed three purpose-built AI agents: an Ingester for multi-source data collection, a Reconciliation Engine for pattern matching, and a Command Center for orchestration and oversight..
- Created an interactive dashboard providing visibility into outstanding invoice statuses, incoming email activity, and agent execution in real time.
- Added AI-powered verification to validate candidate matches against master records, before approved data moves into downstream systems.
- Automated workflows using parameter-based triggers to generate collection and activity reports, and deliver working files to cloud storage.
Tech Stack
- Multi-Agent Orchestration Framework
- AI Agents: Ingester, Reconciliation Engine, Command Center
- Large Language Models (LLMs)
- Enterprise REST APIs & Email Parsing
- Cloud Storage Integration
Impact
- Reduced manual effort across data matching and cash allocation workflows.
- Near-real-time visibility into invoices, transactions, and reconciliation activity.
- Validated, traceable workflows with built-in verification and audit visibility.