Ensuring data reliability through artificial intelligence and robust systems is crucial for compliance and efficiency in the digital transformation of fund management and distribution, noted Joerg Grossmann, chief product officer, and Maxime Aerts, head of asset management product strategy at FE fundinfo. Based in London and Luxembourg, respectively, they shared their insights during the latter part of a two-part Q&A with Delano. FE fundinfo is a financial data company specialising in fund information, technology and services.

Kangkan Halder: What are the primary obstacles to digital transformation in fund management?

Joerg Grossmann and : Firstly, each actor in the distribution chain tends to operate within its own technical environment, with locally replicated databases, limiting data sharing and potentially leading to inconsistencies or time differences. Secondly, there is a focus on the business outcome, like a new report to produce, with data management often being an afterthought. This approach must shift towards a data-first perspective and data-sharing models to ensure smoother processes and better, more consistent outcomes.

By focusing on the data quality we can improve overall efficiency and ensure quality outputs throughout the lifecycle of the product, from registration to national competition authority transmission, from document production to dissemination, from data publication to sharing with counterparties.

With the constantly evolving legal and regulatory landscape, how do you stay ahead in providing compliant regulatory reporting solutions to your clients?

Our dedicated team focuses on regulatory horizon scanning, constantly monitoring regulatory changes to ensure we are always prepared. Additionally, we have a specialised fund registration and filing team, which navigates the complex local requirements for fund setup and distribution across different jurisdictions.

The team comprises experts who understand the legal language and the practical implications for financial products. This expertise lets us stay proactive and help our clients navigate regulatory changes effectively by translating them into actionable steps.

Are there any upcoming regulatory changes that will impact asset management and fund distribution?

In 2024, we expect a focus on sustainability and greenwashing regulations, particularly in the UK, where new anti-greenwashing rules are being implemented. These rules require rigorous labelling and disclosure, impacting both sustainable and non-sustainable fund managers. In the European Union, the review of the SFDR regulation is anticipated, though the specific changes will depend on the outcome of upcoming elections.

Additionally, we foresee changes from the European Securities and Markets Authority in the coming years, particularly regarding the technical standards for data and reporting, which aim to streamline information provision to the public.

Another significant regulation currently creating a new challenge is the Digital Operational Resilience Act (Dora), which emphasises digital resilience and outsourcing, affecting how asset managers interact with third-party providers.

What measures do you implement to ensure the reliability of data?

Our robust system, paired with a large team of experts focusing on data quality and consistency, are paramount in ensuring data reliability. We accept data in any form from asset managers and then map it to our data fields, validating and providing feedback to ensure continuous improvement.

Our approach includes using AI to automate data quality checks and cross-referencing information from various sources. We also work closely with clients to improve the quality of their data from the source to the required business outputs. This collaborative approach helps eliminate inconsistencies and ensures end-to-end quality, which is crucial for regulatory compliance.

Could you share concrete examples of how you integrate AI into your processes and are there any risks associated with AI that asset managers should be aware of?

We use AI to enhance internal processes, such as automating data mapping and quality checks, which were previously done manually. This increases efficiency and improves data quality. AI helps in various stages, from ensuring data quality at the source to automating cross-checks and facilitating the selection of investment products based on complex criteria. By leveraging AI, we can offer our clients better and more efficient solutions.

One significant risk is the quality of the data used in AI systems. If the input data is incorrect, the AI-generated outputs will also be flawed. Asset managers must ensure that the data they provide is accurate and up-to-date.

Additionally, asset managers need to differentiate their products effectively to ensure they stand out in AI-driven selection processes. Proper data management and marketing strategies are essential to maintain visibility and relevance in an increasingly automated environment.

Is there anything else you would like to add about the trends and challenges in asset management?

Innovation will continue to be a key focus for asset managers. We expect to see more activity in active ETFs and private markets. Technology will enhance distribution and investor engagement, especially as we cater to digital-native and technology-driven investors.

Standardisation and convergence of regulations across different jurisdictions are also important trends. As Esma pushes for more uniform standards, asset managers will need to adapt to ensure compliance. Our goal is to provide consistent and efficient solutions that meet these evolving requirements.