Account Reconciliation

Transaction matching and exception resolution automate account and bank reconciliation using AI agents and chatbots.

Business impact

• Month-end close time — Reduced by automating transaction matching and exception resolution, speeding close cycles
• Reconciliation accuracy — Improved through AI-driven matching and anomaly detection, reducing errors and omissions
• Payroll processing time — Decreased by over 50% via automated payroll reconciliation workflows
• Manual effort in reconciliation — Lowered by automating rules maintenance and exception handling, freeing staff for higher-value tasks

Data requirements

• General ledger and bank transaction data (Structured) — Used for matching transactions and identifying discrepancies during reconciliation
• Historical reconciliation records (Structured) — Provide training data for predictive matching and anomaly detection models
• Invoice and payroll data (Structured) — Support automated payroll and supplier invoice reconciliation processes
• User input and exception notes (Text) — Enable conversational agents to assist with investigation and resolution

AI methods and techniques

• Agentic AI — Autonomously performs transaction matching, rules maintenance, and exception resolution
• Predictive AI — Forecasts likely matches and flags anomalies based on historical patterns
• Generative AI — Drafts collection letters and reconciliation explanations to support communication

AI models and model families

GPT-4o, Claude, Workday Illuminate™, Microsoft Dynamics 365 Account Reconciliation Agent

Industries

Real-world evidence

3 documented case studies on record.

Companies using this: FloQast, Genpact, Lifetime Products.