Financial Services Firm Automates Large-Scale Derivatives Data Reconciliation
At a glance -
the essentials
Leading Financial Services Firm
- A leading financial services firm faced an unsustainable data reconciliation challenge. Massive volumes of derivatives data were generated daily across multiple source systems in varied formats - DAT, Excel, and JSON - with real-time changes adding further complexity. Manual validation was time-consuming and error-prone, and QA validation during SIT/UAT was taking 5–6 hours per run, creating a critical bottleneck in every release cycle.
Kumaran implemented a DQV-based automated data reconciliation framework to replace the manual process entirely. The solution enabled advanced filtering, aggregation, and comparison logic across DAT, Excel, and JSON datasets. Virtual alert fields were introduced to surface issues instantly, and real-time insights were built in to accelerate defect identification - giving QA teams immediate visibility into data discrepancies without manual triage.
- 100% data coverage with no sampling required
- Validation time reduced from 5–6 hours to ~15 minutes per run
- 1.5 GB files with 800K records compared in just 153 seconds
- Real-time defect identification replacing manual triage
- Scalable across DAT, Excel, and JSON data formats
From hours of manual validation to minutes of automated precision
A leading financial services firm partnered with Kumaran to overhaul derivatives data reconciliation, cutting validation run times from 5–6 hours to approximately 15 minutes and achieving 100% data coverage. The engagement delivered a DQV-based automation framework capable of processing 1.5 GB files containing 800,000 records in under three minutes. For this firm, the challenge was never just speed - it was the accuracy, completeness, and traceability required for derivatives data at enterprise scale, where errors carry significant financial and regulatory consequences.
The Background
Like many large financial institutions, the firm had accumulated a complex web of source systems generating derivatives data daily in multiple formats. Manual validation had become the norm, but it was unsustainable - time-consuming, inconsistent, and unable to keep pace with data volumes. SIT/UAT cycles were consuming entire business days on validation alone, delaying releases and stretching QA team capacity. The need was clear: automate reconciliation comprehensively, with full coverage and real-time defect visibility, without disrupting ongoing operations.
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