Data Integrity Issues in Banking: Major Compliance Challenges and Solutions

Overview of Data Integrity Challenges in Banking

Banks face a high cost when data errors slip through due to inadequate data control. Examples include fines for TD Bank, Wells Fargo, and Citigroup due to failures in anti-money laundering controls and data management. The main reasons for these data issues include non-scalable data processes, unrealistic expectations of subject matter experts (SMEs), difficulty reconciling data across platforms, resistance to modern solutions, and the inability to detect broken data pipelines. To address these problems, banks should adopt a “fingerprinting” approach, which continuously monitors data patterns and trends, providing a safety net like a seatbelt. DataBuck, an AI assistant to autonomously monitor data quality, offers features such as automated rule recommendations, continuous trust monitoring, secure architecture, and scalability. By adopting these modern solutions, banks can reduce regulatory risk and enhance compliance while lowering costs.

The High Costs of Data Integrity Issues for Banks

Prestigious banks with strong IT teams are unable to trust their data. Errors, even if its occasional, in their decisions and reports are extremely costly financially and to their reputation.

• TD Bank was fined $6.7 million by Canada’s anti-money laundering (AML) agency for failing to submit suspicious transaction reports and properly assess money laundering risks.
• Wells Fargo faced enforcement action from the OCC for deficiencies in its anti-money laundering controls, requiring the bank to enhance its compliance programs.
• Citigroup was fined $136 million by U.S. regulators for insufficient progress in addressing longstanding data management issues identified in 2020.

Common Causes of Data Quality and Compliance Issues in Banking

Despite sensible controls why do so many errors get through? The primary reasons for these data integrity challenges include:

1. Non-scalable Data Control Processes: Traditional data control processes often lack the scalability needed to handle large volumes of data efficiently. Legacy tools like Informatica are extremely laborious and not designed for users to set up and validate 1,000’s of tables. They take man-years to do it not man-days.
2. Unrealistic Expectations of Subject Matter Experts (SMEs): SMEs are typically responsible for writing data quality rules. However, they will not always foresee all potential issues, leading to gaps in data quality and integrity.
3. Inability to Reconcile Data Across Platforms: As data moves from one platform to another, inconsistencies invariably arise, making it difficult to maintain data integrity.
4. Resistance to Adopting Modern Solutions: There is often resistance to adopting machine learning- based solutions, which can automate and enhance data quality processes.
5. Detection of Broken Data Pipelines: Identifying and addressing broken data pipelines is crucial, yet many organizations struggle with this, leading to data integrity issues.

Fingerprinting: A Scalable Approach to Data Monitoring for Banks

To address these issues in a scalable way banks must adopt the fingerprinting approach to monitoring data trust. It is powerful as it tracks all patterns, relationships and trends in the data. It monitors both important attributes and the seemingly unimportant ones. It is the equivalent of a seatbelt. Would you decide that certain roads are safe, and not put a seatbelt on your child? Even though you may be okay most of the time, it’s unsafe and unacceptable. The seatbelt is there to protect you from theunexpected. The best practice is to always wear the seatbelt because of the unknown-unknown risks. Banks should install automated, autonomous data trust monitoring solutions.

DataBuck: AI Assistant for Autonomously Monitoring Data Trustability

DataBuck offers several key features that significantly enhance data integrity and compliance efforts:

1. Automated Rule Recommendations: Powered by AI/ML, DataBuck auto-discovers and recommends essential and advanced data quality rules for each dataset. These baseline set of rules can be complemented with additional custom rules as needed, ensuring reliability and accuracy.
2. Continuous Trust Monitoring: The validation rules are auto coded to an executable form. DataBuck then continuously monitors data stores and pipelines. It creates circuit breakers to stop data errors from cascading to downstream applications.
3. Secure Architecture: DataBuck moves the rules to where the data is and, not move the data itself. All data, results and computations are within the user’s firewalls. Data never leaves the company’s control. This ensures the highest levels of data security.
4. Scalability: DataBuck autonomously validates 1,000’s of datasets with just 3 clicks, reducing data maintenance-work and costs.

By adopting solutions like DataBuck, banks can overcome data integrity challenges, ensuring better compliance and reducing the risk of regulatory fines, while keeping the TCO at 1/10^th of the traditional approach.

Elevate Your Organization’s Data Quality With DataBuck by FirstEigen

DataBuck enables autonomous data quality validation, catching 100% of systems risks and minimizing the need for manual intervention. With 1000s of validation checks powered by AI/ML, DataBuck allows businesses to validate entire databases and schemas in minutes rather than hours or days.

To learn more about DataBuck and schedule a demo, contact FirstEigen today.

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