Inconsistent Fund Data as an Underestimated Risk
Fund data is often seen as individual data points: a risk indicator in the EMT, sustainability information in the EET, the cost structure in the PRIIPs KID, fee parameters in the prospectus, or historical performance data based on historical NAVs. In practice, however, the picture is often more complex: only when these data points are viewed in combination it becomes clear whether fund data is truly consistent and plausible. This is exactly where a risk arises that is often underestimated.
Individual data points may appear correct in isolation but may be inconsistent across different sources. A fund may be shown with a specific SRI value in the EMT, while the PRIIPs KID indicates a different risk class. Sustainability-related information in the EET may deviate from the sector allocation or product presentation. Cost components may be presented differently in the PRIIPs KID than in the EMT or prospectus. Similarly, a product may be classified as suitable for investors with limited loss-bearing capacity, even though its historical performance shows significant drawdowns.
Such discrepancies are not merely a technical data quality issue. They can affect distribution, product selection, reporting, internal controls, and regulatory traceability. At the same time, they represent a risk not only for banks, insurers, platforms, or other data recipients, but also for the fund management company itself. Inconsistent fund data can trigger queries from distribution partners, create manual clarification efforts, complicate operational processing, and, in unfavourable cases, increase reputational or audit risks.
Particular focus is placed on fund data relevant for distribution and regulatory purposes from different sources: EMT, EET, PRIIPs KID, prospectus, and historical performance data. Particular attention is paid to risk indicators, target market information, sustainability information, fee parameters, and historical NAV data.
Especially for larger fund universes, a manual review of such interdependencies is hardly scalable. Different update cycles, multiple documents per fund, and numerous data points increase the risk that inconsistencies are not identified at all, or only at a late stage.
A structured consistency check can help make such anomalies systematically visible. The key question is not only whether individual data parameters are available, but whether they are plausible and traceable in relation to one another. This allows discrepancies to be identified early, prioritized, and reviewed in a targeted manner.
In this way, consistency becomes a measurable quality criterion. Fund data must not only be complete and up to date. It must also be plausible, consistent, and traceable across different sources.
Fenion’s services support this process by identifying and assessing inconsistencies and helping to prevent them at an early stage.
