Data Quality Tools for Finance: Requirements in the AI Era

As regulatory expectations rise and AI becomes embedded in risk and finance processes, traditional data quality tools are reaching their limits. Financial institutions now need solutions that can operate continuously, explain their outputs, and withstand regulatory and internal scrutiny.

This shift is reflected in the way the market evaluates data quality tools. Opensee, for example, has been recognised for the second consecutive year as Best Data Quality Analysis Tool by the Data Management Insight Awards, first in Europe and now in the US. This recognition is not only about specific features; it also reflects how requirements for data quality management in finance have evolved.

This article outlines what modern data quality tools must deliver for financial institutions and how an end-to-end, AI-enabled approach changes what is possible.

1. Why traditional data quality tools are no longer sufficient

Legacy data quality approaches were designed for a different operating environment. They typically rely on batch processing and end‑of‑day checks, static rules and thresholds, isolated profiling and reconciliation tools, and manual investigations or spreadsheets to resolve issues. 

These methods increasingly struggle in today’s context, where risk, PnL, and capital calculations are refreshed frequently (often intraday), regulatory regimes such as BCBS 239 require consistent, traceable data across multiple systems and entities, and AI models and advanced analytics depend on high‑quality, explainable data to avoid amplifying underlying issues.

In this environment, data quality tools must move from periodic control to continuous oversight, embedded into the data lifecycle rather than added as a final layer. The focus shifts from detecting obvious errors after the fact to detecting, prioritising, and resolving issues in near real time.

2. AI-driven anomaly detection as a core capability

Anomaly detection is one of the areas where AI and advanced analytics can add immediate value in data quality. Traditional approaches typically rely on simple rules (for example, ranges or percentage changes). In complex trading books and risk datasets, this is not enough.

Modern data quality tools for finance increasingly use machine learning and robust statistical techniques to:

• Learn “normal” behaviour from historical data rather than depending solely on fixed rules
• Detect outliers, structural breaks, and unusual combinations of values in real time
• Highlight anomalies that are truly material for risk, PnL, capital, or reporting, instead of generating large volumes of low-value alerts

Opensee’s approach is an example of this trend. Our platform embeds an AI framework that identifies anomalies and outliers as data is ingested and processed, helping firms catch issues before they feed into critical calculations. When anomalies are found, users can apply guided adjustments and AI-based estimations to derive plausible values, within governed workflows, preserving dataset integrity while maintaining a clear audit trail.

For risk and data teams, this enables a more targeted workload. Instead of screening large datasets manually or reacting only after reports fail validation, they can focus on the subset of data points most likely to affect key metrics.

3. Continuous consistency monitoring across systems and portfolios

Many impactful data quality problems are not single incorrect values, but inconsistencies across systems, time periods or business functions. These are particularly common in capital markets and bank risk environments, for example:

• Differences between NPV and risk metrics for the same portfolio
• Missing or inconsistent risk factors affecting sensitivities or capital charges
• Misalignments between risk, finance, and front office views of PnL or exposure

Addressing this requires data quality tools that can monitor internal consistency across multiple dimensions, not just validate individual fields in isolation.

Opensee does this with ongoing automated checks and low/no-code data quality controls. Business rules can be defined to express expected relationships – for example, the way certain risk measures should move relative to PnL under normal conditions – and then applied continuously across portfolios and time.

Opensee’s AI Driller capability goes further by helping users identify the drivers behind changes in PnL, risk, capital, and portfolio performance. Instead of prolonged reconciliation cycles, teams can quickly drill down by desk, book, instrument, risk factor, or time period to isolate the source of discrepancies.

This level of consistency monitoring is increasingly viewed as an important requirement for data quality tools in regulated financial environments.

4. Standardisation and certification aligned with regulatory principles

Regulatory expectations around data governance, especially under frameworks like BCBS 239, have raised the bar for data quality and certification processes. Firms can no longer rely on fragmented, team-specific controls that vary by report, jurisdiction or business line.

Modern data quality tools need to support a standardised certification process that:

• Detects inconsistencies, duplicates, and errors in real time, rather than relying exclusively on manual cleansing
• Provides risk and finance teams with access to granular metrics and long histories in a controlled environment
• Exposes configurable data quality indicators – such as completeness, timeliness and accuracy – in clear dashboards aligned to regulatory concepts
• Applies a common set of data quality rules and thresholds across multiple teams and outputs, reducing divergence and reconciliation effort

Opensee’s platform includes an end-to-end certification framework with these characteristics. Data can be standardised, validated and certified within one environment, and the same controls apply across different risk, finance, and reporting use cases. This helps institutions industrialise their data quality processes and more easily demonstrate consistent practices to regulators and internal stakeholders.

5. Full auditability and version control as non-negotiable features

Auditability is now central to the design of credible data quality tools in financial services. Institutions must show not only that data is accurate and consistent at a point in time, but also how it reached that state.

This requires:

• Clear histories of all changes to datasets, including who made them and why
• The ability to compare versions of datasets over time
• Support for maintaining multiple variants of a dataset (for example, for backtesting, scenario analysis or model validation), while still preserving a single official reference

Opensee incorporates a Git-like versioning system that automatically records all datasets modification, from small corrections to complex simulations. Users can inspect differences between versions, revert when necessary, and keep parallel dataset variants for analysis. This level of traceability supports:

• Internal and external audits
• Model risk management and validation
• Regulatory reviews where institutions must evidence the lineage behind reported numbers

In the context of AI, this is particularly important. As models become more complex and data-driven decisions more frequent, institutions need to demonstrate that both models and input data are governed under robust, transparent processes.

6. Managing the full data lifecycle rather than isolated steps

One of the main limitations of many existing data quality tools is their narrow scope. They may focus on profiling, running rules, or providing dashboards, but they do not cover the entire data lifecycle. Large financial institutions, however, face data quality issues at every stage:

• Ingestion and preparation of raw, often heterogeneous data from multiple sources
• Application of quality checks, anomaly detection, and business rules
• Standardisation and aggregation for risk, finance, and regulatory use
• Certification and sign-off processes
• Ongoing access for analysis, drill-down and simulation, with full traceability

Opensee’s platform is designed to cover this lifecycle end-to-end for risk, finance, and regulatory data use cases. It combines scalable data management with embedded AI-driven quality controls, consistency checks, certification workflows, and audit capabilities in a single environment.

This integrated approach allows:

• Risk teams to rely on the inputs behind metrics such as VaR, FRTB capital charges, XVA and other complex calculations
• Finance teams to reduce manual reconciliation effort around PnL, capital and balance sheet reporting
• Data teams to enforce policies and standards without becoming a bottleneck, as business users can work within controlled self-service frameworks

In practice, this moves data quality from a reactive, control-focused function to a more proactive foundation for decision-making and regulatory compliance.

Opensee’s back-to-back recognition as Best Data Quality Analysis Tool in Europe and the US illustrates how these capabilities are increasingly expected from modern data quality tools in finance.

7. Next steps

Data quality is moving from an operational concern to a critical enabler of risk management, regulatory compliance, and AI adoption. Institutions that continue to rely on fragmented, manual or purely rule-based approaches are likely to face growing operational and regulatory risk.

By contrast, organisations that adopt end-to-end, AI-enabled data quality tools can:

• Detect and prioritise issues earlier
• Reduce manual intervention and reconciliation work
• Improve confidence in risk and finance outputs
• Support more ambitious analytics and AI initiatives on a solid data foundation

Opensee works with leading financial institutions to implement this type of architecture, from raw data ingestion through to certified, auditable outputs. To explore how such an approach could support your own risk, finance and data strategies, contact us for a demo.