Balancing Fairness, Accuracy, and Explainability in Machine Learning Models for Credit Decisioning
By Graydon Goss As machine learning (ML) reshapes the landscape of credit decisions, the stakes around lending fairness become much more than an…
Inspiring Confidence in Financial Modeling with Automated Data Quality Monitoring
While incorporating artificial intelligence and machine learning (AI/ML) into financial modeling can help your financial institution unlock valuable new insights that strengthen your portfolio, these tools also heighten the need for confidence in the reliability of your data. AI/ML models often rely on a more expansive volume and variety of data, and their outputs are only as strong as the quality of the information they analyze.
Ensuring your data is accurate, complete, and consistent can be a complex challenge, and failing to do so can create substantial costs. Research from Gartner estimates that, each year, companies lose an average of $15 million in revenue due to poor data quality.
How confident are you in the reliability of your data? According to a recent survey from Capgemini, 80% of banking executives view this question as a top concern. By using technology to evaluate data quality on a continuous basis, automated data quality monitoring (ADQM) can alleviate uncertainty and allow you to identify and resolve issues before they reach your models.
Data Quality Roadblocks
Maintaining quality data is no easy task. Financial institutions face several complex challenges that stand in the way of maintaining quality across the data lifecycle:
- Data Volume and Complexity: Financial institutions collect a substantial amount of data from a broad range of sources. This includes internal data on transactions, customers, accounts, risk, and compliance as well as external data from markets, credit bureaus, regulatory agencies, and third-party providers. This variety makes it difficult to establish consistent levels of quality across the institution and ensure all data is subject to strict quality standards.
- Unstructured Data: Unstructured data presents unique storage, maintenance, and security challenges. Data such as emails, documents, customer service notes, and social media information tend to be disorganized and inconsistent, requiring sophisticated data cleaning and preprocessing techniques.
- Data Integration and Data Merging: Merging data from multiple sources and integrating data from different systems can introduce quality issues throughout the data lifecycle. This can be especially difficult when onboarding data after a merger or acquisition as different institutions often have varying data management conventions and procedures.
- Data Governance: Many financial institutions lack an organizational culture that treats data as an asset that demands proper care and maintenance. Ensuring consistent quality requires comprehensive adherence to an enterprise-wide set of standards, policies, and procedures as well as data ownership that fosters accountability for maintaining data integrity.
- Third-Party Data: Financial institutions often rely on third-party data providers that may have weaker quality standards. However, despite having less control over the collection and management of external data, the financial institution maintains the same responsibility to ensure quality as it does with data produced and collected internally.
Costs of Poor Data Quality
Unreliable data can be extremely disruptive for financial institutions. Failure to monitor quality can impose severe consequences on all corners of the enterprise including investment, risk management, operations, and compliance.
Poor Financial Performance
Financial institutions increasingly rely on data-driven insights for decision-making. Poor data quality can hinder the ability to analyze market trends to inform investment decisions, understand market demand to develop and market new products, form accurate customer profiles to provide attentive service, and forecast to set long-term strategic goals.
Risk Management Weaknesses
Assessing and managing risk with inaccurate or incomplete data on counterparties, investments, or market conditions can make it difficult to gauge the true level of risk associated with various assets or activities. This can lead an institution to mismanage capital and liquidity and create exposure to financial losses and economic volatility.
Increased Operational Costs
Planning and executing operations with poor data can lead to inefficiencies such as payment and transaction errors, resource misallocation, and process delays. Financial institutions may need to allocate more time and resources to manually correct data entry errors, reconcile discrepancies, and resolve system failures.
Regulatory Non-Compliance
Many financial institutions must routinely report key financial metrics to regulatory authorities. Inaccurate submissions can result in regulatory scrutiny, potential fines, and reputational damage. Institutions may also face reporting requirements related to Anti-Money Laundering (AML) and Know Your Customer (KYC) regulations.
FI’s Approach
FI leverages decades of experience working with financial data and technology to develop custom automated monitoring solutions for clients that enhance the accuracy, completeness, and consistency of their data. Our solutions help financial institutions identify and address data quality issues in real-time, promoting confidence in the reliability of data-driven insights and financial reporting. FI’s approach relies on the following set of core components:
FI’s Impact: Custom ADQM Solution for a Top 10 Bank
Recently, FI developed and implemented an in-house ADQM solution for a large (top 10 in the US in total assets) banking client. The client needed to ensure that data moving downstream from its central repository through a critical ETL pipeline was accurate, complete, and consistent.
FI developed a system that seamlessly integrated with the pipeline and evaluated data quality daily based on three classes of rules tailored to the client’s specific needs:
- Accuracy: Is the data correct? Are the values close to what we would expect? Does the data have the proper distribution?
- Completeness: Is all necessary information available and recorded, or is there missing data?
- Consistency: Does the data have the proper format? Is the data consistent with recent history?
The screenshots below illustrate some of our solution’s key features, including user-friendly dashboards, quality metric tracking over time, and granular views of specific datasets with several filtering options.
We designed the solution to produce an alert for any rule violation and direct the user to the specific value responsible for the violation. Additionally, we built a dashboard that assigned a rating (e.g., green, yellow, or red) to each data element, enabling easy visualization of the quality of the dataset. The solution expanded the client’s ability to rapidly identify and resolve data issues and swiftly elevated the quality of data flowing through the pipeline.
After implementation, the solution enhanced the client’s monitoring of thousands of data elements, many of them critical inputs to high-risk models. The client was immediately alerted to existing data quality issues and was able to quickly diagnose and remediate the problems. The client also obtained key performance indicators (KPIs) contributed to enhanced monitoring of data accuracy, completeness, and consistency. This raised confidence in model output that fed into sensitive regulatory reporting.
Are you ready to take control over the quality of your data? Whether you’re looking for an enterprise-wide solution or something that fits the needs of a specific business unit, we can help you build the right in-house tool. Email us at contact@ficonsulting.com or call 571.255.6900.
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