About a-team Marketing Services
The knowledge platform for the financial technology industry

A-Team Insight Blogs

Alveo Reviews Costs and Use Cases of AI in Financial Data Management

Subscribe to our newsletter

Adoption of AI across financial services is causing a cost shift from operations to technology and data, with 63% of decision makers expecting AI to result in an increase in the cost of data within their organisation. The cost of hardware and software licences is also likely to rise in response to AI, and 50% of decision makers note technological limitations among the biggest barriers to implementing AI in financial data management, 46% reference a lack of skilled personnel.

On the upside, according to research commissioned by Alveo that surveyed senior decision-makers at financial services organisations in the UK, US and DACH region (Germany, Austria and Switzerland), AI offers huge potential to drive productivity across data management with 53% of the sample ranking data quality management as the area of data management where AI will have the greatest impact.

In terms of today’s use of AI, the survey found financial services firms using AI for different aspects of financial data management, with 55% of firms using it for risk data management, 49% for client data management, 47% for portfolio data management and 46% for master data management.

Commenting on the results, Martijn Groot, vice president of marketing and strategy at Alveo, says: “As the human element in data workflows diminishes due to the next wave of automation, there is a large premium on good quality data. To achieve and maintain the high standard of data quality necessary for effective AI implementation, firms will need financial data management expertise to design, oversee, and refine the infrastructure and processes that feed into AI systems, and ensure all data is accurate, relevant, and timely.”

Subscribe to our newsletter

Related content

WEBINAR

Recorded Webinar: The Data Office at a Crossroads — AI Governance, Organisational Design, and the Evolving Mandate of the CDO

Who owns AI governance in a capital markets firm – and is the Data Office structured to bear that weight? These questions sit at the heart of A-Team Research’s latest findings, presented here for the first time: the combined results of two landmark surveys examining the role of the Data Office in AI governance and...

BLOG

CFTC File Format Change to Impact Futures Data Management Teams

For futures commission merchants, clearing members, proprietary trading firms, and banks with material futures and options exposure, the transition of CFTC Part 17 Large Trader Reporting to FIX Markup Language (FIXML) is a test of data management maturity. This change directly affects firms responsible for aggregating, validating, and submitting large trader position data, often across...

EVENT

Eagle Alpha Alternative Data Conference, Fall, New York, hosted by A-Team Group

Now in its 8th year, the Eagle Alpha Alternative Data Conference managed by A-Team Group, is the premier content forum and networking event for investment firms and hedge funds.

GUIDE

AI in Capital Markets Handbook 2026

AI adoption in capital markets has moved into a more disciplined phase. The priority is now controlled deployment: where AI can be used safely, where it can deliver measurable value, and how outputs can be governed, monitored and evidenced. The 2026 edition of the AI in Capital Markets Handbook examines how AI is being applied...