No More Mismatches: Automate & Streamline Your Reconciliation Process
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Check out the core features that make reconciliation process seamless, accurate and reliable.
Compatible with XLSX, PDF, CSV, XLS and XLSB files - enabling seamless integration with internal systems, external partners, and legacy tools.
Secure and intuitive web portal to track file uploads, monitor reconciliation, and download reports.
Designed for offering logical isolation, secure access, and centralised management for multiple clients.
Engineered to handle millions of records on daily basis, ensuring performance at enterprise-grade workloads.
Supports customisable statuses in reconciliation (like Matched, Mismatched, etc.), offering flexibility to align with business processes.
Supports complex reconciliations involving two or more data sources, enabling deeper validations such as bank-to-ledger-to-subledger comparisons.
Allows multiple types of reconciliations under a single account, while offering segregation for access and visibility.
Designed to deliver high precision in matching with minimal false positives or missed matches.
Uploaded a wrong file? No problem. Instantly undo reconciliations and fix mismatches with rollback support.
Supports fuzzy logic, rule-based, and custom matchers to reconcile when data is messy, incomplete, or inconsistent.
Enabling accurate, rule-driven reconciliation across complex and varied data sources.
In complex reconciliation scenarios, relying on a single field for matching records across data sources may not be sufficient due to incomplete information. Multi-column matching offers a more robust and accurate approach by comparing a combination of fields—such as transaction date, amount, reference number, and beneficiary details—to establish a stronger basis for identifying matching records.
Before accurate reconciliation can occur, raw data often needs to be normalized and transformed into a consistent format. Differences in date formats, currency symbols, casing, or numeric precision can all hinder effective matching. Our reconciliation framework allows pre-processing of records to ensure consistent and comparable values across sources.
In real-world scenarios, a single logical record may be split across multiple rows—especially in systems that track partial payments, split settlements, line items, or batch-level operations. Our reconciliation framework supports intelligent multiple rows merging, enabling accurate and context-aware matching of such fragmented data.
Not all records align perfectly on the primary key or preferred matching fields. Our reconciliation framework includes a fallback column strategy, allowing graceful degradation of matching logic when exact matches aren’t found in the primary column.
Our reconciliation framework offers intelligent Multi-Step Matching that mimics real-world matching logic—starting with the most reliable identifiers and progressively relying on secondary parameters to ensure maximum match coverage.
Our framework begins by reconciling records based on a highly reliable primary column (e.g., Transaction ID). If no match is found, it falls back to a secondary column (e.g., Reference Number or Narrative) to attempt resolution.
Handling complex reconciliations across sectors - efficiently and accurately, enabling you to seamlessly processes high volumes with unmatched consistency.
