At first, poor financial data may appear to be a technical or reporting issue, often seen in inconsistent figures, delayed reports and manual reconciliations. However, the true cost is much wider. Poor data impacts reporting efficiency, regulatory confidence, planning and makes senior decision making more difficult.
In financial services and insurance businesses, the quality of financial data has become an increasingly important operational and strategic concern. Finance teams are expected to produce accurate reporting while supporting regulatory submissions and management information that informs long-term planning. When the underlying data is difficult to reconcile, the impact is felt across the organisation.
The issues may not always be visible initially; poor data quality is often absorbed through additional checks, spreadsheet adjustments and time and effort spent explaining variances. Over time, a hidden cost is created, reducing confidence in the information being used to make decisions.
One of the most immediate concerns of poor financial data is the pressure it places on reporting processes. Across month-end reports, regulatory submissions, and management information, accurate and consistent source data is key to represent the true picture.
When this is not available, finance teams have to spend more time checking figures and rebuilding confidence in numbers, leading to slower reporting cycles and a greater reliance on manual intervention. Teams may need to trace figures across various systems, investigate unexplained movements or review data that should already be aligned. While this work is necessary, it often consumes time and effort that could otherwise be spent on strategic work, such as review, analysis and future planning. If numbers require repeated clarification, or if different teams are working from different versions of the same information, management information becomes harder to interpret and less effective as a basis for action.
In regulated financial services, poor financial data can create more serious consequences. Regulatory reporting relies on firms providing accurate, consistent and timely information, and weak data quality can make it harder to evidence financial resilience, governance and control.
In its review of prudential regulatory reporting by MIFIDPRU investment firms, the FCA found that, while most firms understood their reporting requirements, a significant proportion had made errors or showed recurring weaknesses in their regulatory returns. The FCA also highlighted that poor quality data can make identifying weak financial resilience, risks and weaknesses in systems more difficult.
In regulatory environments, poor data may require further investigation, resubmission, additional review and additional manager oversight. It can also weaken confidence in the firm’s reporting capabilities, particularly when errors recur or figures cannot be clearly traced to their original source.
Firms are expected to provide better data, with clearer ownership and stronger controls around how it is produced. As a result, the Bank of England and PRA have been working to modernise regulatory data collection, with a focus on improving the relevance, quality and timeliness of data while reducing the cost and burden on firms.
Poor financial data also affects business planning and decision making. Particularly for finance teams, unreliable data can impact an organisation’s confidence in areas such as forecasts, scenario modelling and performance analysis.
In turn, senior teams may find it difficult to make accurate commercial and strategic decisions, as data may require further investigation before use. Plans made using poor data may result in plans being challenged, creating further delay. In more complex organisations, the same issue can appear across several reporting cycles, particularly where data is being drawn from multiple systems or adjusted manually before it reaches the final report.
When faced with repeated poor data quality, finance teams risk spending more time on correcting, reconciling and explaining data, limiting their ability to support long-term strategies and reducing the value that financial information brings to the wider business.
To improve data quality, firms need to go beyond correcting errors at the point of reporting. There should be clear ownership of data, agreed definitions, reliable controls and processes that can be easily traced. Without this, finance teams can continue to absorb the cost through manual workloads and additional checks.
Strong financial data management should cover the full reporting process, from the way data is captured and validated through to how it is reconciled, reviewed and used in management information. Where firms rely on multiple systems, legacy processes, and manual adjustments between finance, actuarial, risk and operational teams, this is especially important.
The key areas for improvement include:
Clear ownership of financial data across teams and systems
Consistent data definitions and reporting standards
Strong reconciliations and controls
Reduced reliance on manual adjustments
Better alignment between finance, risk, actuarial and operational reporting
A more structured approach can reduce manual workarounds, improve reporting confidence, and offer senior leaders better sources that inform decision making.
We work with clients to improve financial capabilities and operations, with a focus on accuracy, speed, fewer errors and better management information. Our finance transformation support is designed to give senior management sufficient time for review, decision making and value creation, while strengthening how financial information is produced and used.
We help organisations across finance transformation, actuarial, risk, compliance and wider change programmes to review their existing processes, improve reporting, and align financial information with strategic objectives.
Improving financial data requires clear ownership, practical controls and a finance function that can provide accurate information with confidence. Our experienced consultants work alongside teams to develop practical, proportionate solutions that improve capability and support sustainable change.
Speak to us about building a more accurate, reliable and resilient approach to financial data.