Data quality in the financial sector: how to avoid losses

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Did you know that poor quality data can cost financial companies a lot? Learn how to ensure data quality and avoid losses.
In the financial sector, accurate, timely and complete information is crucial for both companies and customers. Poor data quality can have severe consequences in terms of financial losses, reputation and even legal implications. Data management and technological advances have the potential to minimize these risks, generate great advantages and incorporate new information into decision-making. What are their main contributions?





According to a survey of data professionals, data nurse database issues impact 25% or more of revenue.

Source: The State of Data Quality 2023



The importance of Data Quality in the financial sector
Maintaining data quality in the financial sector is important for several reasons:

Risk management: Data is used in models to predict credit risk, market volatility and fraud to support investment strategies and loan approvals and minimise damaging decisions.

Regulatory compliance: Data is crucial for compliance reporting and avoiding penalties. Regulatory agencies use the information to assess an institution’s compliance.

Customer satisfaction: Data helps to correctly identify customer needs, preferences and behaviors to personalize the services and solutions offered, avoiding dissatisfaction and attrition.

Operational efficiency: Clean, accurate data increases the efficiency of everyday processes by reducing the time and resources spent on data verification and correction. It minimizes errors in transactions, billing, and customer communications, thereby reducing operational costs.

Competitive advantage: Accurate data enables financial institutions to gain useful insights to improve services, enter new markets, or launch new products. Poor quality hampers these strategic decisions and makes it difficult to outperform competitors.




Poor data quality can result in up to 60% of business decisions being based on inaccurate data.

Source: Decube



How to avoid losses by improving data quality?
Poor data quality not only creates operational inefficiency, but also poses risks to the integrity, performance and credibility of financial institutions. Fortunately, there are a set of procedures and practices in place to ensure quality information and minimize the risks associated with poor data quality:

Establish a data governance framework: By defining policies, standards, and responsibilities related to management, the foundation for data quality is laid. Guidelines for data collection, storage, use, and standardization of procedures are defined to ensure consistency and accuracy.