In the utility services sector, companies process large volumes of payments and invoices daily. Manual reconciliation introduces inefficiencies, errors, and delays that directly impact cash flow visibility and financial accuracy. To scale effectively and maintain compliance, the company needed a solution to automate reconciliation and exception handling while supporting long-term growth.
Challenge
The company's bank reconciliation process required heavy manual validation of large data sets, often exceeding hundreds of thousands of rows.
- Teams relied on invoice numbers, PO references, and date checks to match payments with charges, but manual work caused delays, bottlenecks, and higher error risk. Exception handling lacked consistency, making audits and reporting time-consuming.
Solution
The company implemented Decisions to automate its complex reconciliation workflows. Using layered business rules, the platform applied dynamic matching logic based on PO numbers, token IDs, invoice data, and macro-level date checks.
- High-volume data uploads supported reconciliations exceeding 700,000 rows.
- Exception management workflows allowed for claims, overrides, and provider data updates.
- An admin dashboard enabled real-time visibility into reconciliation progress, exceptions, and reporting.
- Historical tracking and archival ensured long-term data access and audit readiness.
Differentiators
Decisions was selected for its ability to handle complex reconciliation logic at scale while remaining adaptable:
- Rules Differentiators: Centralized business rules apply layered matching logic while allowing governance and experimentation.
- Flow Engine Differentiators: No-code workflows ensure rapid configuration of exception handling and claims management.
- Integrations: REST endpoints and smart database integrations allow seamless ingestion of CSI and payment data from multiple sources.
- Testing: Built-in unit testing and sample data validation reduced risk during deployment.
- Platform Attributes: Hybrid deployment and modular extensibility supported both performance and long-term scalability.