Modern enterprises process a large number of digital transactions monthly, each one incurring its own fees. These include gateway fees, MDR (Merchant Discount Rate), interchange, cross-border, chargebacks, and others. With multiple payment partners pushing through complex and often opaque decentralized (non-standardized) fee structures, even minor errors can snowball into millions of dollars of financial loss annually if these go untracked. Traditionally, validating these fees required written, manual reconciliation; using excel spreadsheets, line-item by line-item comparisons, tracking down discrepancies, etc. This was not only time-consuming, but also prone to human error.
The shift from manual to automated auditing processes
Manual audit of payments is fraught with problems that can severely impact the bottom line of an organization. Problems include things like data-entry-related errors, missed discounts from vendors, higher risks of fraud, and time-consuming reconciliations, just to name a few. Research suggests that as much as 50% of duplicate payments result from employee error and approximately 25% of organizations lose out on early payment discounts because of manual processing delays. Not only do these problems drive up operational costs, they increase an organization's exposure to risk. So, more than 40% of organizations now see automation as a necessary strategy to streamline workflows, improve costs, and reduce the possibility of fraud in the accounts payable and payment auditing space.
Core drivers for automation
- Accuracy: Automated systems validate every transaction, reducing errors and revenue leakage.
- Speed: Real-time reconciliation and anomaly detection accelerate the audit cycle.
- Compliance: Automated audit trails and compliance checks ensure regulatory adherence.
- Cost savings: By eliminating manual tasks, enterprises save on labor and avoid costly mistakes.
How enterprises achieve transaction-level fee validation
1. Data collection and integration
Modern audit applications connect to ERP, billing, and banking platforms, automatically collecting payment data from various sources of payments (including invoices, purchase orders and receipts, or electronic payment documentation). In practice, a large multinational retailer can aggregate purchasing data from hundreds of point-of-sale (POS) systems, aggregating the data for a comprehensive view of activity across the enterprise.
2. Automated validation and reconciliation
AI and machine learning tools can cross-check payment information against contracts, purchase orders, or receipts, and report exceptions in real time. This cross-checking means every fee, charge and payment is validated at the transaction level—independently of each other.
3. Real-Time analytics and anomaly detection
Automated systems provide a continuous review of payment data, using independent algorithms to find anomalies including over-billing, payments not approved, and outlier fees. Rather than forcing the finance team to comb through every transaction, dashboards and reporting show the finance team if exceptions occur, directing their review only to any exceptions.
4. Comprehensive audit trails and reporting
Automation platforms generate detailed, immutable audit trails for every payment-related activity, supporting compliance with regulations like SOX, GAAP, and GDPR. Automated reports are customizable for different stakeholders, ensuring transparency and readiness for external audits at any time.
