A Global Financial Institution Standardised 10M+ Financial Records
About The Client
The client is a large banking and financial services institution managing significant volumes of transactional, customer, account, lending, payment, and financial data across multiple business units. Information was distributed across core banking platforms, payment systems, lending applications, finance systems, databases, and legacy environments. As data volumes increased, inconsistencies between systems made reconciliation, reporting, and analysis increasingly resource-intensive. The organisation needed a structured financial data management model to improve data quality, standardise critical information, strengthen governance, and create a more reliable foundation for financial operations, reporting, and analytics.
0M+
Financial Records Managed
0+
Data Sources Consolidated
0%+
Data Accuracy
0%
Reduction in Data Exceptions
The Challenge
The institution generated large volumes of financial data every day across customer accounts, payments, loans, transactions, reconciliations, and other banking activities.
However, data had accumulated across multiple systems over time. Similar information could be represented differently between core banking applications, finance platforms, operational databases, and reporting environments.
Incomplete records, duplicate information, inconsistent formats, and mismatched identifiers created additional processing effort. Teams frequently needed to validate and reconcile data before it could be used confidently.
Legacy systems added further complexity. Historical financial information remained important for operations and reporting, but integrating it with newer platforms required careful mapping and standardisation.
Data governance was another priority. The organisation needed clearer definitions, ownership, validation rules, and controls around critical financial data elements.
Without a consistent data management framework, reporting teams spent considerable time preparing information before analysis, while operational teams dealt with recurring data issues across downstream processes.
What Did KGS Do
KGS began by assessing the institution's financial data landscape, source systems, record structures, data flows, quality issues, reporting requirements, governance practices, and existing controls.
More than 10 million financial records across 30+ source systems were brought within structured data management workflows.
KGS standardised priority customer, account, transaction, payment, lending, and financial information around agreed data definitions and formats. Validation controls helped identify incomplete records, inconsistent values, duplicate information, invalid data, and mismatched identifiers.
Data matching and reconciliation workflows were introduced to identify differences between related records across banking and finance systems. Exceptions were categorised and routed to the appropriate teams for investigation.
KGS also helped establish clearer data ownership, quality rules, metadata, and governance practices around critical financial information. Ongoing monitoring provided visibility into data quality trends, recurring exceptions, and areas requiring remediation, helping the institution move from periodic data correction towards continuous financial data management.
The Results
- Managed and standardised 10M+ financial records
- Consolidated information from 30+ banking and finance data sources
- Maintained 99%+ accuracy across targeted financial datasets
- Reduced recurring data exceptions by 45%
- Improved consistency across customer, account, transaction, payment, and lending information
- Created a stronger data foundation for financial reporting, analytics, and operational decision-making
What did the client say?
“KGS helped us bring much greater consistency to financial information that had accumulated across different systems over time. Data issues are now identified through structured validation and reconciliation processes, and our teams spend less time manually preparing information for reporting. We have a much more reliable foundation for both operational and analytical use.”
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