Leading data practitioners have urged financial institutions to ensure they have suitable data management and infrastructural setups to accommodate artificial intelligence (AI) applications following a report that suggested asset managers are struggling to roll out the technology.
The latest in an annual study by professional services giant KPMG found that while asset managers in the US are increasingly adopting AI-based processing in the back- and middle-offices, it said that they are “still struggling to achieve full maturity due to a lack of clear vision, as the tactical approach of focussing on specific use cases rather than end-to-end processes hinders capabilities being fully integrated into business operations across the enterprise”.In response, executives at two leading data companies, Finbourne and Clearwater Analytics, suggested that while full implementation of AI is being held back the process could be rectified by focussing on data-related processes.
Low Confidence
Nick Wood, AI product manager, at Finbourne said that robust data management structures are a key factor in a successful AI transition.
“It’s clear that emerging technologies like AI are becoming increasingly relevant in asset management. However, many companies are still in the early stages of leveraging AI with serious adoption remaining slow,” Wood said.
“This hold up is largely due to a lack of confidence in the incumbent data management processes, which need to be designed to support AI technologies.”
AI adoption has become the subject of intense scrutiny within the data ecosystem as institutions slow their initial rush towards implementation and vendors stress the need to take a strategic approach to any transformation towards the nascent technology.
Last month data management provider Informatica encouraged firms to look at their governance policies before widescale AI integration and NeoXam, among others, said that it was crucial that data quality concerns be addressed first.
Slow Rollout
KPMG’s report, its Asset Management Industry Outlook for 2025, said that 61 per cent of the more than 100 individuals questioned had prioritised educating their workforces in their AI rollout preparations. Half said they were focussing mainly on encouraging employees to experiment with the technology within guardrails.
Consequently, AI maturity has shifted a little from the conceptual stage to the developmental stage over the past year. The focus of that change has largely been on generative AI (GenAI), which is being used mainly in the back-office for tasks related to communications, finance and accounting, the study said.
Nevertheless, the survey found that more than half of asset managers said they were being held back by data integrity, statistical validity and model accuracy issues. A lack of awareness and training as well as the risk of security vulnerabilities were also cited as impediments.
KPMG is taking a keen interest in AI development after it said last year it would invest more than US$2 billion to deploy the technology’s capabilities for its clients. The company has signed a partnership deal with Microsoft to develop GenAI tools.
Quality Issues
The findings in the company’s latest report were in tune with those revealed in a separate study by a S&P Global Market Intelligence Cappitech Survey late last year, which said similar data quality failings were hampering regulatory reporting decision making.
“While AI can certainly act as a feature and capability in an overall workflow, firms must be able to explain the models and trust the quality of the underlying data to get there,” said Finbourne’s Woods. “With AI showing so much promise, prioritising modern data infrastructures to address data quality concerns will be a priority for firms that wish to stay competitive.”
Human Power
Clearwater’s Colin Clunie, head of EMEA operations, said that despite the struggles being faced by some organisations to implement AI, the firms that were progressing apace had managed to do so because they had struck a balance between machine- and human-powered processing.
“It is clear that the industry is leaning towards AI adoption, with successful use cases demonstrating the power of AI in transforming operations and creating efficiencies by simplifying entire investment life cycles,” Clunie said.
“As clients seek quicker data delivery and greater accuracy, particularly during periods of market volatility, AI can be a panacea for meeting these needs. This isn’t to say there aren’t risks attached to any new technology, which is why human oversight is such a crucial factor in successfully applying AI, as it can control and verify results, detect anomalies, and provide a clear view of decision-making processes.”
Authors of the KPMG report said that asset management firms needed to take a holistic approach to GenAI, recommending they ask how they can balance the risks of adoption against the opportunities it presented.
“Firms that can effectively integrate GenAI across their entire operation from back-office functions to front- and middle-office departments… such as portfolio optimisation and fund management, are poised to outperform their competition,” it concluded.
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