As banks accelerate their digital transformation journeys, the reliance on high-quality reference data has never been more critical. From client onboarding and credit approvals to trade settlements and regulatory reporting, accurate data is the foundation for smooth operations and compliance readiness.
Financial institutions have faced steep regulatory penalties due to failures in Anti-Money Laundering (AML) frameworks, often driven by poor reference data quality. Organisations have been fined heavily for their lapses in the recent past. Inaccurate or siloed data can delay suspicious activity detection and complicate compliance reporting. This makes strong financial data quality management a business-critical priority. Yet, reference data, such as legal entity identifiers, instrument codes, and product hierarchies, often suffers from inconsistency, duplication, and fragmented ownership.
As compliance mandates like MiFID II, Basel III, and FATCA grow more stringent, and as digital channels multiply customer touchpoints, the cost of poor data quality rises, financially and reputationally.
why reference data quality remains a challenge
Despite widespread adoption of automation tools and data lakes, many financial institutions still struggle with persistent data quality issues. These include legacy processes, siloed systems, inconsistent data formats, and manual intervention across departments. These are all well-documented challenges in improving reference data quality in banking:
- Siloed systems: Departments operate independently, maintaining separate databases with different formats and standards.
- Inconsistent identifiers: Clients, instruments, or products often have multiple identifiers across systems, increasing reconciliation workloads.
- Manual intervention: Data entry during onboarding or trade processing is still heavily manual, leading to errors and time lags.
- Lack of accountability: Without designated data stewards, discrepancies often go unnoticed until they cause downstream failures.
- Data explosion: The increasing volume and velocity of data, from market feeds to customer inputs, make maintaining quality at scale difficult.
These issues not only affect operational efficiency but also pose regulatory risks and damage customer trust.
six key steps to improve financial data quality
A structured, phased approach to financial data quality management helps organisations move from reactive fixes to proactive governance. Here are six steps aligned with industry best practices:
define ownership through data stewardship
Appoint data stewards for each domain, client, product, and instrument, who can be held responsible for issue resolution, quality assurance, and cross-functional coordination.
embed domain-specific validation rules
Integrate automated validation checks at the point of entry. For example, validating client or product data at entry reduces downstream errors and improves traceability.
standardise data classification and formats
Implement consistent definitions, naming conventions, and coding standards across departments. This minimises translation errors and enables better interoperability between systems.
establish a golden-source reference data hub
A central repository serves as the authoritative source for critical reference entities, reducing data duplication and enabling seamless updates across downstream applications.
automate quality monitoring and alerts
Use rule engines or machine learning models to continuously scan for anomalies, such as duplicate records, missing fields, or outdated values, and trigger timely interventions.
incorporate data quality gates into workflows
Make quality control a part of operational routines. For instance, client onboarding and trade confirmation workflows should include mandatory data completeness and accuracy checks.
These steps also map to the four-phase Reference Data Management (RDM) maturity model observed across capital markets: data assessment, standardisation, consolidation, and continuous monitoring.
why good reference data drives better outcomes
Clean reference data is not just an IT hygiene factor. It delivers measurable advantages:
- Regulatory compliance: Accurate data ensures audit-readiness, reduces penalty risk, and simplifies regulatory reporting processes.
- Operational efficiency: Reduces manual exception handling and reconciliation efforts, freeing teams to focus on higher-value tasks.
- Improved risk models: Clean data feeds into better credit, market, and liquidity risk models, enhancing overall financial stability.
- Stronger customer experiences: Faster onboarding, fewer service errors, and personalised interactions are enabled by reliable information.
- Faster decision-making: Executives and analysts can trust reports and dashboards, allowing for quicker, data-driven decisions.
Improving reference data quality in banking is central to how banks manage risk, meet compliance mandates, and deliver customer value.
Financial institutions that treat data quality as a continuous capability, not a one-time project, will be better equipped to scale, innovate, and thrive in a data-driven future.
how can Infosys BPM support reference data transformation?
Infosys BPM helps financial institutions improve data quality through deep domain expertise and automation. With future-focused workforce and technology expertise, Infosys BPM helps design and implement robust, scalable data quality frameworks that align with regulatory and business priorities. Our capital markets solutions are developed to embed data quality into everyday operations.
