How financial services could tackle data quality issues?

According to recent Gartner research, organizations report an average annual loss of $15 million due to poor data quality. Like an engine fails with the wrong fuel, data is the lifeblood of banking, insurance, and investments.

For financial institutions, bad data can cause significant operational and economic setbacks. Poor data quality not only increases costs through remediation efforts but makes gaining leadership buy-in for data-driven initiatives more difficult. Even minor errors can lead to breaches of trust and catastrophic outcomes. Misplace a single digit, mislabel a counterparty, or use an outdated CCP code, and the consequences can go far beyond compliance headaches—potentially leading to multi-million dollar fines and regulatory scrutiny.

Citibank’s $500 million penalty for unresolved data governance issues is a stark reminder of the financial and reputational risks that come with neglected data quality.

Now, let’s delve into the most pressing data quality challenges financial institutions face and how modern data quality solutions, can provide much-needed relief.

Regulatory compliance

Financial operations hinge on standardized fields that document transactions precisely, ensuring compliance with complex regulatory frameworks. These fields, encompassing everything from currencies to securities, provide the backbone for accuracy and interoperability across global financial systems. Adherence to regulations like GDPR, CCAR, Basel III, FATCA, and EMIR depends on the uniformity and reliability of this data.

However, misreporting — whether through missing or misformatted data — can lead to severe penalties and a breakdown in trust. Manual validation is impractical and error-prone given the vast volumes of data financial institutions process daily.

For instance, a single incorrect currency code can distort financial analyses, impair risk assessments, and trigger regulatory non-compliance.

Modern data quality tools are no longer optional but essential to mitigate these risks. AI-driven anomaly detection and continuous monitoring would provide financial institutions with a proactive defense, ensuring data discrepancies are swiftly identified and corrected. This approach helps maintain compliance standards and shields organizations from regulatory scrutiny.

Reporting, analysis, and forecasting

Data quality forms the foundation for reporting, from routine analyses to complex risk assessments and forecasting. Inaccuracies, particularly in vast volumes of real-time data, can distort risk calculations and skew the strategic forecasts that inform future investments and policy decisions. The quality of data used to train AI models directly impacts the insights these models can provide for predicting credit risk, market volatility, and potential fraud. These insights guide critical decisions—everything from loan approvals to investment strategies. Poor data, however, can lead to catastrophic decisions, such as lending to high-risk clients or making unwise investments, resulting in financial losses that could have been avoided.

Consider a scenario where the sum of total payments for each payer stays within 25% of the historical average for the past 20 days. If this consistency fails, it could signal anomalies, leading to incorrect financial reporting, misaligned risk assessments, or even regulatory non-compliance—putting the organization at significant risk.

To address these challenges, implementing comprehensive data quality solutions is crucial as they can streamline the creation of data quality rules through intuitive profiling capabilities and an interactive, user-friendly interface.

Data governance

The financial sector’s rapid shift to cloud technology represents a fundamental transformation from traditional, in-person services to digital-first approaches. Cloud adoption offers immense benefits, including increased agility, scalability, and operational efficiency. However, this shift introduces significant challenges, particularly in maintaining data integrity during migration between different storage systems and platforms.

Effective data governance is crucial in mitigating these risks. Governance policies outline clear roles and responsibilities, including data ownership, access, and security protocols, ensuring that data is properly managed throughout its lifecycle. These policies are critical in highly regulated industries like banking, where data security and compliance are non-negotiable.

When integrated within a robust data governance framework, advanced data quality solutions become indispensable in tackling these challenges. Such solutions offer capabilities that go beyond traditional governance, including:

• Streamlined workflows for proactive data issue resolution: By identifying potential issues before they escalate, these solutions ensure smoother operations and reduce the risk of costly data-related mistakes.
• Ensuring data integrity during migrations: Automatic profiling of data sources and destinations helps maintain accuracy and completeness throughout migration processes, safeguarding data from corruption or loss.
• Embedding data quality into pipelines: Features like circuit breakers and data quality binning ensure a continuous, reliable flow of high-quality data through pipelines. These embedded workflows automatically detect and resolve anomalies, keeping data consistent and compliant with governance standards.

Operational efficiency

As financial institutions expand through mergers and acquisitions, they often face the challenge of consolidating data from disparate legacy systems, each with different formats, quality standards, and levels of completeness. Ensuring data integrity during these migrations is critical to maintaining operational efficiency. Financial institutions risk operational disruptions and poor decision-making without reliable, accurate data.

Modern data quality solutions automate the critical tasks of data profiling, cleansing, and validation, ensuring that data is accurately assessed before, during, and after migration.  

By continuously monitoring the data throughout the migration process, these solutions ensure that no data is lost, corrupted, or altered, safeguarding the integrity and reliability of information as it moves between systems.

Moreover, as institutions scale, the necessity of open architectures becomes clear. Open architectures enable seamless integration across diverse systems, ensuring data is accessible at every stage. This accessibility is vital to maintaining operational efficiency as organizations grow, allowing them to adapt to new data sources and evolving business needs without compromising data quality.  For insights on maximizing the value of your data, explore How to Maximize Dividends From Your Data.

Conclusion

The complexity and volume of data in finance today demand not just vigilance but automation. Employing automated data quality solutions not only streamlines the maintenance of data quality but elevates it, allowing banks and financial institutions to not just react to data issues but anticipate and prevent them. This technological leap forward is where solutions like Telmai come into play.

Telmai’s edge lies in its user-friendly interface and the deployment of AI-driven anomaly detection, utilizing advanced machine learning algorithms to foresee and proactively address potential data quality issues. With its intuitive dashboard, users can effortlessly visualize data health, trace lineage, and understand quality metrics, empowering financial institutions to operate with the highest data integrity and analytical insight.

Take a proactive approach to mitigate risks and ensure accuracy in financial operations. Click here to try Telmai today.

Passionate about data quality? Get expert insights and guides delivered straight to your inbox – click here to subscribe to our newsletter now.