Improving Data Quality to Leverage AI

Financial services firms are increasingly turning to artificial intelligence (AI) to gain a competitive edge. However, the effectiveness of these AI systems hinges critically on the quality of the data they use.

According to a report by MarketsMedia, one study found that 66% of banks struggle with data quality due to gaps in important data points. Furthermore, organizations are currently mostly on their own in their efforts to produce high-quality, understandable, and usable data. This is due in part to a lack of defined frameworks from regulatory bodies, but it is also due to the newness of artificial intelligence.

"Data quality is largely a function of the collecting organizations’ governance, policies, data architectures, infrastructures, and practices,” says a report published in the International Journal of Law and Information Technology.

"Inadequate architectures, infrastructures, and practices undermine these organizations’ ability to accumulate high-quality data.”

Here, we’ll explore how financial institutions and financial services firms can improve the quality of their data so they can leverage future applications of AI.

Current Data Quality Challenges

Financial services firms face numerous data quality challenges that can impede their ability to effectively leverage AI.

Inconsistent Data

One of the most pressing issues is the prevalence of inaccurate, incomplete, or inconsistent data, which can significantly impair the performance of AI models and lead to unreliable outputs and decisions. This is particularly critical in the financial sector, where decisions based on faulty data can have severe consequences.

Data Bias

Data bias presents another significant challenge. Biased datasets can lead to AI models that perpetuate existing prejudices, which is especially problematic in applications such as loan approval or risk assessment. For instance, historical lending data may reflect past discriminatory practices, potentially leading to unfair outcomes if not properly addressed.

Privacy and Security Issues

Privacy and security concerns also pose substantial hurdles. With stringent regulations like GDPR and the increasing threat of data breaches, ensuring the privacy and security of data used in AI operations is paramount. Financial institutions must navigate the complex landscape of data protection while still maintaining the utility of their data for AI applications.

Lack of Data Accessibility

Accessibility and sharing of high-quality, relevant data can be challenging due to proprietary restrictions and technical barriers. This limitation can hinder the potential for AI models to learn from diverse and comprehensive datasets, which is crucial for developing robust financial models and risk assessment tools.

Inaccurate Data Labeling

The issue of data labeling is particularly relevant in supervised learning applications, which are common in financial services. Accurate labeling is crucial, but manual labeling is often time-consuming and expensive. For example, correctly labeling transactions as fraudulent or legitimate requires significant expertise and resources.

Decaying Relevance of Data

Financial institutions also grapple with the challenge of "data drift,” where the statistical properties of the target variable change over time in unforeseen ways.

Common causes of data drift include:

  • Changes to data collection processes upstream
  • Broken data collection points
  • Natural drift due to changing market conditions
  • Changes in the relations between figures ("covariate shift”)

Addressing this issue is especially pertinent in finance, where market conditions and consumer behaviors can shift rapidly, potentially rendering AI models less accurate if not regularly updated.

Strategies for Data Quality Improvement

Addressing the above challenges requires a multifaceted approach, including implementing robust data control policies, leveraging advanced data cleaning and validation techniques, and fostering a data-centric organizational culture.

Here are the key strategies that firms can implement.

  • Data Standardization: Standardize formats, labels, and units across datasets, especially when merging data from multiple sources. This ensures consistency and facilitates accurate analysis by AI models. For financial data, this might involve standardizing transaction codes, currency formats, or customer identifiers.
  • Regular Auditing: Conduct periodic reviews of data quality to identify new issues as they arise. In the financial sector, this is particularly crucial due to the dynamic nature of market data and regulatory requirements.
  • Data Enrichment: Enhance existing datasets with additional information from reputable external sources to fill gaps and extend usefulness. For example, augmenting customer data with third-party financial indicators or market trends can provide more comprehensive insights for AI-driven risk assessments.
  • Bias Mitigation: Implement bias mitigation techniques at various stages of the AI/ML lifecycle, including pre-processing, in-processing, and post-processing of data. This might involve re-weighting training data or adjusting model outputs to ensure fair representation across different demographic groups in financial decision-making processes.
  • Data Anonymization and Pseudonymization: Protect individual privacy while maintaining data utility by removing or replacing personally identifiable information in datasets. This is crucial for compliance with financial regulations and maintaining customer trust.
  • Encryption and Access Control: Implement strong encryption standards for data at rest and in transit and establish strict access control measures using role-based access control (RBAC) and the principle of least privilege. This ensures that sensitive financial data is protected from unauthorized access.
  • Collaborative Platforms: Utilize collaborative platforms that support data sharing and version control, facilitating teamwork on data-driven projects within the organization. This can improve data consistency and reduce errors introduced by siloed data management practices.
  • Expert Annotation: For complex financial data requiring domain expertise, involve subject matter experts in the annotation process to ensure high-quality labels for supervised learning models. This is particularly important for tasks such as fraud detection or risk assessment.
  • Automated Labeling Tools: Leverage software that can semi-automatically label data, reducing manual effort and improving consistency. These tools can be particularly useful for processing large volumes of financial transactions or documents.

By implementing these strategies, financial services firms can significantly enhance the quality of their data, leading to more reliable and effective AI applications in areas such as fraud detection, risk management, and personalized financial services.

Creating an AI-Ready Data Infrastructure

In addition to the strategies listed above, building an AI-ready data infrastructure is crucial for financial services firms aiming to harness the full potential of artificial intelligence. This involves creating a robust framework that supports efficient data collection, storage, processing, and analysis while ensuring data quality, security, and accessibility.

Cloud-Based Infrastructures

A foundational element is the implementation of scalable data architectures that can handle large volumes of diverse data types. Utilizing cloud-based solutions offers flexibility and scalability, allowing firms to adjust resources as needed without significant upfront investments.

Cloud platforms also facilitate real-time data processing and analytics, which are essential for timely decision-making in fast-paced financial environments.

Data Integration Pipelines

Data integration is another critical aspect. Financial institutions often deal with disparate data sources, including transactional data, customer information, and market indicators. This not only enhances data consistency but also improves the accuracy of AI models by providing comprehensive datasets for analysis.

Implementing advanced ETL (Extract, Transform, Load) processes ensures seamless integration of these varied datasets into a unified system.

According to IBM, "ETL data pipelines provide the foundation for data analytics and machine learning workstreams. Through a series of business rules, ETL cleanses and organizes data to address specific business intelligence needs, such as monthly reporting—but it can also tackle more advanced analytics, which can improve back-end processes and end-user experiences.”

Data Governance Frameworks

Data governance frameworks are essential for maintaining high standards of data quality and compliance with regulatory requirements. These frameworks should include policies for data stewardship, quality control measures, and clear protocols for data access and sharing.

Implementing automated monitoring tools can help detect anomalies or drifts in data quality over time, allowing for proactive adjustments to maintain model accuracy.

Third-Party Solutions

Finally, investing in cutting-edge technologies such as AI-driven data management tools can further optimize infrastructure readiness. These tools can automate routine tasks like data cleaning and labeling, freeing up human resources for more strategic activities.

Two examples of such technologies are the products developed by Talend and Alteryx, which we’ll explore below.

Data Validation with Talend

While improving data quality can be challenging to do in-house, service providers like Talend offer tools to clean and validate data, helping companies make the most of AI applications by ensuring only high-quality data feeds into machine learning models.

"With massive amounts of data streaming in from multiple sources, a data cleansing tool is more important than ever for ensuring accuracy of information, process efficiency, and driving your company’s competitive edge,” said Talend in a blog post.

The organization’s "Data Quality” product automatically alerts users of errors and inconsistencies while organizing data validation tasks into a simple workflow on a single platform. Still, its tool is "only one part of an ongoing, long-term solution to data cleaning.”

Data Preparation and Insight Generation with Alteryx

Software company Alteryx provides financial services firms with automated analytics that are intuitive to use. It also provides data preparation capabilities to firms so they can simplify regulatory reporting and compliance, leverage large datasets, and modernize financial processes.

Alteryx even launched its own AI tool called AiDIN. According to a blog post, this AI engine "infuses machine learning and gen AI across the Alteryx Analytics Cloud platform.” The tool enables organizations to expeditiously generate predictions and recommendations, discover new patterns in data, automate repetitive content creation tasks, and make data analytics processes more transparent and auditable.

Future Considerations of Leveraging AI in Financial Servies

As financial services firms continue to embrace AI, future considerations must focus on evolving data management practices and ethical AI deployment. Prioritizing data diversity and bias mitigation will be crucial to ensuring fair and unbiased AI systems, particularly in sensitive areas like credit scoring and fraud detection.

Investing in continuous learning systems will also be vital. These systems can adapt to new data inputs and changing market conditions, maintaining AI model relevance and accuracy over time.

Finally, collaboration across the industry can lead to shared best practices and standards for AI implementation, fostering an ecosystem that supports sustainable growth and innovation in financial services. By addressing these considerations, firms can harness the full potential of AI while navigating the complexities of an ever-evolving technological landscape.

To learn more about how your organization can improve data quality and more effectively leverage artificial intelligence, don’t miss FIMA US 2025. It takes place from April 7th to April 8th at Westin Copley Place in Boston, Massachusetts.

View the agenda and register for the event today.

Return to Blog