Systemic handling of regulatory data – Are capital management companies on an equal footing with CRR institutions?

In the increasingly data-driven world of finance, asset management companies (KVGs) are faced with the challenge of meeting regulatory requirements efficiently and systematically. The topic of data management is increasingly coming into focus – both in terms of quality, availability and security as well as with regard to the strategic use of data.

Looking at the banking sector, the advantages of holistic data strategies, central governance structures and the reduction of data silos have been emphasized for some time. Institutions in particular see this not only as a way to meet regulatory requirements, but also as a basis for data-driven innovations. At the same time, there is a growing recognition in the industry that data is viewed as a strategic asset.

The KVGs lack a correspondingly clear marker and the range in development is likely to diverge widely. But are fund companies really at a disadvantage compared to institutions when it comes to handling regulatory data?

1. Changing regulatory requirements

For institutes, the focus in 2025 will be on topics such as CRR III, DORA, CSRD, FiDA and MiCAR. These regulations require a high level of data quality, availability and security. Institutions have responded with massive investments in data governance, data lineage and automated reporting systems.

AIFMs are also increasingly affected – for example by CSRD (Corporate Sustainability Reporting Directive), SFDR (Sustainable Finance Disclosure Regulation) and AIFMD II. Or when special investor groups pass on their regulatory reporting requirements largely unfiltered and the KVG thus have to become experts in Solvency II,, CRR III and the GroMiKV. There is growing pressure to manage ESG data, risk metrics and reporting packages in a consistent, traceable and audit-proof manner, for example.

2. The status quo in asset management companies

While institutions were forced to standardise at an early stage by requirements such as BCBS 239, the regulatory maturity of AIFMs varies greatly. Many institutions have begun to modernize their data architectures in recent years. Nevertheless, there are still structural differences to the banking sector:

• Fragmented system landscapes: While institutions often use centralized data warehouses, fund companies often work with isolated solutions – especially for smaller companies.
• Manual processes: Excel-based workflows are still widely used in the fund industry, which increases the susceptibility to errors and makes auditability more difficult.
• Lower IT budgets: Compared to institutes, AIFMs have significantly fewer resources for data-driven transformations.

3. Data management from the perspective of asset management companies

Compared to institutions, asset management companies are confronted with specific challenges that manifest themselves in several dimensions:

Data Architecture

Many KVGs work with decentralised system landscapes that have grown over time. A consistent data architecture is often missing, which leads to media discontinuities and redundant data storage. The development of central data platforms is often still in its early stages.

Regulatory maturity

While institutions were forced to standardise at an early stage by requirements such as BCBS 239, the regulatory maturity in the administration of AIFs varies greatly. ESG reporting, SFDR and AIFMD II are driving professionalization, but uniform standards are missing.

Data Governance

Roles such as “data owner” or “data steward” are not yet established in many companies. There is a lack of clear responsibilities and documented processes for data quality, maintenance and responsibility.

Reporting Capability

Regulatory reporting is often still carried out in “batch-oriented” processes with manual post-processing. Real-time reporting or automated validation are rare.

Investments in IT systems

Compared to institutes, budgets for data-related IT initiatives are significantly smaller. Investments are usually reactive – for example, to meet new regulatory requirements – and only rarely, unfortunately, strategically forward-looking.

These differences mean that many AIFMs are not yet on an equal footing with institutes when it comes to systemic data handling – even if the will to transform is increasingly noticeable.

4. Fictitious practical example: Solvency II reporting in TPT 7.0 format

At the beginning of 2025, a medium-sized KVG that manages several special funds for insurance companies was confronted with increasing requirements in the context of Solvency II reporting. The Tripartite Template (TPT), which was developed by the BVI in cooperation with EIOPA, was adapted to version 7.0 and the requirements for data consistency, completeness and up-to-dateness increased even further.

Until now, KVG did not have a central data platform. The information required for the TPT – for example on look-through data, risk ratios, derivatives or ESG characteristics – was manually merged from various files and systems (portfolio management, risk controlling, external data suppliers). This led to:

• A high level of coordination between departments;
• inconsistent data;
• and delayed deliveries to insurance clients.

One solution could be to introduce a rule-based data quality framework that automatically checks and validates the TPT-relevant data. In addition, a central data model could be developed that also integrates the requirements from other reports, e.g. regulatory reports. These two projects alone could accelerate data deliveries in any case and significantly reduce the error rate in reporting (with comparatively little effort).

Conclusion: Need to catch up with potential

Capital management companies are not yet on an equal footing with institutions when it comes to the systemic handling of regulatory data. The reasons lie in historically grown structures, lower budgets and a later regulatory focus. But the change is noticeable: the requirements are increasing, and with them the willingness to transform.

The decisive question is therefore no longer whether AIFMs need to manage their data systematically – but how quickly they will manage to do so at a level comparable to that of institutions.