Case study
Large-Scale Financial Data Reconciliation Platform
How I helped a financial services organization reconcile 50+ datasets with higher accuracy and faster turnaround.
Context
A large financial services organization aggregated transaction data from more than 50 upstream systems into downstream reporting and regulatory platforms. These datasets were owned by different teams and evolved independently, leading to inconsistencies, mismatches, and reporting risk.
The problem
Inconsistent identifiers, formatting differences, and mismatched records created reconciliation backlogs, delayed reporting, and reduced confidence in downstream data.
What I built
I designed a reconciliation workflow that normalized, matched, and validated data across 50+ datasets, while tracking over 200 reconciliation items in a centralized control system.
How it worked
- Standardized identifiers and formats across all datasets
- Applied deterministic matching and validation rules
- Flagged missing, duplicated, or inconsistent records
- Tracked issue ownership and resolution status
- Generated operational and leadership reports
Impact
- ~10% increase in reconciliation completion rate
- ~95% automated matching accuracy across multi-million-row datasets
- ~30% reduction in downstream data errors