The Importance of Data Quality for Financial Services

Banks and insurers handle vast amounts of customer data. Ensuring this data is accurate is crucial, as incorrect information can lead to poor investment decisions, regulatory issues, and loss of customer trust. Our research shows that businesses collectively lose £24.9 billion each year due to inaccurate or incomplete customer data, impacting billing.

Read on to learn about data quality in the finance sector, why it is important, and how financial services can improve customer data quality.

What is data quality in the finance sector?

In the finance sector, data quality refers to the accuracy, completeness, reliability, and timeliness of customer data. This means ensuring customer information is correct, up-to-date, comprehensive, and trustworthy for decision-making.

Financial organisations rely heavily on accurate customer data for key tasks like generating reports, approving loans, detecting fraud, and assessing credit risks. If the data is poor quality, it can significantly disrupt these processes, leading to consequences for both the business and its customers.

To address these issues, financial organisations need to ensure their customer data is of high quality. This involves using processes and technologies that clean, integrate, and manage customer data from various sources, supporting both day-to-day operations and strategic planning.

Our business and data solutions for financial services can help address these issues with tailored solutions to improve data quality.

Why is data quality important in finance?

Data quality is vital for financial services because it ensures accurate decision-making and compliance with regulations, helping to avoid costly errors and maintain customer trust. High-quality customer data is essential for providing personalised services, preventing fraud, and maintaining operational efficiency.

Here are some key examples of why data quality is so important in finance:

1 Mitigate Risk

Accurate customer data is essential for assessing creditworthiness and making informed lending decisions. High-quality data reduces the risk of default by ensuring that loans are given to creditworthy customers and that investment decisions are based on reliable information.

For instance, quality data can verify credit scores, income levels, employment history, existing debts, and repayment histories. Reliable data allows lenders to accurately calculate debt-to-income ratios, assess financial stability, and identify any red flags that might indicate a higher credit risk.

Explore our customer credit risk management solutions to enhance your decision-making processes.

2 Prevent fraudulent activity

Poor data quality makes financial organisations vulnerable to fraud. Accurate and clean data helps put businesses in a much stronger position to detect and prevent identity theft, fake applications, and other fraudulent activities. High-quality customer data allows businesses to:

• Identify inconsistencies: spot discrepancies in customer information that could indicate fraud, such as mismatched addresses or inconsistent personal details
• Monitor behavioural patterns: accurately track customer behaviour over time to identify unusual activities, such as sudden changes in spending habits or multiple loan applications in a short period
• Validate identities: use accurate data to verify customer identities during account opening and transaction processes, reducing the likelihood of fraudulent accounts being created

3 Customer loyalty and satisfaction

Today's customers expect personalised service, which relies on high-quality data. Accurate data helps understand customer needs, preferences, and behaviours, allowing for tailored services. When customer records are matched, merged, and stored in one place, it becomes easier to offer personalised experiences while also protecting customer privacy and security.

Poor data quality can lead to billing mistakes, failed transactions, and incorrect product suggestions, all of which frustrate customers and increase the risk of losing their business.

Customers also expect organisations to handle their data responsibly and securely. Data must be protected from unauthorised access and comply with GDPR regulations to prevent misuse. Failing to meet these standards can result in severe financial penalties and loss of customer trust.

4 Comply with regulatory standards

Financial organisations must comply with regulations such as GDPR and Anti-Money Laundering directives. Accurate customer data is critical for meeting these requirements, ensuring that customer information is handled responsibly and that suspicious activities are reported promptly.

Reliable customer data ensures compliance by:

• Maintaining data accuracy. providing accurate records for audits, reviews, and other documentation required for regulatory compliance
• Facilitating monitoring, allowing businesses to detect and report suspicious activities

Without reliable data, businesses risk failing to meet these regulatory obligations, which can lead to severe penalties and loss of trust.

5 Gain a competitive advantage

In finance, high-quality customer data provides a significant competitive advantage. Accurate and reliable data helps businesses:

• Improve services by understanding customer preferences, tailoring their services to better meet their needs
• Identify new markets and enter them more confidently with data-driven decisions
• Launch new products that resonate with target audiences, reducing the risk of product failure

Conversely, low-quality data hinders these strategic initiatives. Inaccurate or incomplete data makes it challenging to trust insights, leading to missed opportunities and potential losses.

Common data quality issues in financial services

Below highlights typical data quality issues faced by financial services, their impacts, and potential causes. The next section will provide solutions to address these common issues and improve overall data quality.

Incomplete data
Impacts through inaccurate customer profiles, affecting service personalisation and risk assessment.
Potentially caused by missing data entries during data collection, and data truncation during migration.

Incorrect data
Can mislead analytics leading to poor decision-making and compliance risks.
Potentially caused by human error in data entry and/or outdated information.

Duplicate data
Can create increased costs due to redundant data management, as well as skewed analysis.
Potentially caused by multiple entries created across different platforms without synchronisation or a singular view.

Inconsistent data
Creates difficulties in tracking customer interactions across services as well as errors in reporting.
Potentially caused by lack of standard data entry protocols and/or discrepancies in data formats from multiple sources.

Outdated data
Can cause ineffective marketing, poor customer service, and regulatory non-compliance risks.
Potentially caused by infrequent data updates and/or inadequate lifecycle management.

Inaccessible data
Can lead to delays in decision-making, as well as the inability to leverage data for analytics.
Potentially caused by poor data infrastructure and/or restrictive access controls.

Non-integrated data
Can create challenges in obtaining a unified view of customer information, as well as inefficiencies in operations.
Potentially caused by siloed data systems and/of lack of integration initiatives.

How can financial services improve data quality?

Automate processes with data management tools

By investing in data management and analytics tools, financial services can automate many data quality tasks. These tools offer features that enable real-time data checks, automatic error correction, and duplicate removal, significantly reducing the risk of human error.

For instance, our data cleansing platform, Connect, ensures your customer data is accurate and up-to-date via integrated API. We handle goneaway, deceased, and home mover suppression to maintain the integrity of your customer records for marketing.

Regularly audit data

Regular data quality audits are essential to maintaining high-quality customer data. Without regular auditing, it’s possible for new errors and issues to occur. If ignored, these issues will likely impact business processes.

Here are some simple steps for establishing a regulr audit schedule:

• Set a regular audit frequency, such as monthly or quarterly
• Focus audits on key areas like customer information and compliance
• Use automated tools to streamline the audit process
• Review and update your audit procedures regularly
• Document audit findings and take corrective actions promptly

This proactive approach helps businesses clean up their databases, making their data-driven insights more reliable.

Employee training

Improving data quality requires that all employees understand its importance. Training your team on data quality practices can significantly enhance data management processes and reduce the risk of errors.

Employees in roles such as customer service or account management deal with data regularly, so it's vital to implement an effective data quality management strategy across the entire business.

Here are some tips to implement employee training:

• Conduct regular training sessions to keep employees updated on customer data quality standards and best practices
• Implement role-specific training for different positions, such as customer service and account management
• Encourage a data quality culture through internal communications and incentives for good practices

Creating a data governance framework

A data governance framework establishes the rules, standards, and responsibilities for managing customer data. This framework should clearly define how customer data is collected, stored, and used across the organisation.

Understanding the link between data governance and data quality is crucial for effective data management. Data governance provides the framework and guidelines that shape how data is handled, ensuring consistency, security, and compliance. Whereas, data quality focuses on the accuracy, completeness, and reliability of the data itself.

Consider the following when creating a data governance framework:

• Define responsibilities: Who will manage different types of customer data?
• Establish data rules: What rules have been developed for customer data entry, storage, and handling to ensure accuracy and security?
• Check data regularly: How are routine checks implemented to maintain data quality?
• Educate your team: Are all employees informed about their roles in data management and any updates to policies?
• Audit and adjust: How often are data processes audited and refined to ensure compliance and continual improvement?

With clear procedures in place, financial services can keep customer data consistent and accurate.

Conclusion

To sum up, data quality is at the heart of the financial services industry. As data grows more complex and critical to operations, the importance of these efforts will only intensify.

From reducing credit risk, improving customer loyalty, and gaining competitive advantage, quality data helps financial organisations make informed decisions, streamline processes, and ensure compliance with regulatory standards. Prioritising data quality is essential for the success and longevity of any financial institution.

Explore our business and data solutions for financial services

Our business and data solutions for financial services provide data, consulting, and technical services to address the unique challenges in the financial industry. Get in touch with our team to see how we can help you improve your data quality.