Asset managers are increasingly turning to artificial intelligence to support their investment process, according to research from Softwire.
AI has been widely used in back-office admin functions for a number of years. However, it is now making its way meaningfully into investment research, in-house software development and risk modelling.
Softwire gathered insight from 225 technology leaders and found UK asset and wealth managers are entering a ‘second wave of AI adoption.’
Back-office process automation remains the most commonly implemented use case now, selected by 49% of respondents, followed by client-facing AI tools at 46%, automated data extraction at 43% and fraud detection at 42%.
When asked to look ahead respondents painted a different picture, with, AI-assisted software development, AI-assisted investment research, and risk modelling cited as the expected leading use cases, each by 33% of respondents. Back-office automation fell to 21%.
Softwire’s survey also found many in the sector are not yet equipped to make this change at scale.
While 98% have implemented at least one AI proof of concept or pilot into production over the past 12 months, just 12% describe AI as truly transforming their business.
Data and legacy architecture remain major constraints with 81% of the tech leaders saying poor data and legacy systems are limiting their AI progress, and 44% saying their data does not have the quality, lineage and traceability needed for regulatory confidence.
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Sean Judge, director of financial services and Insurance at Softwire said: “That switch is significant.
“Newer models are making more complex knowledge work possible, but higher-value use cases place a much more demanding test on the data, technology, governance and specialist capacity supporting them.”
“Client-facing AI is where the confidence test becomes much harder,” he continued. “Back-office automation can often be kept within a narrower process, but client-facing AI depends on more of the organisation being ready at the same time.
“In a regulated sector, it is not enough for the technology to be impressive. It has to be reliable, explainable and built on data the organisation can trust. That means strong data foundations, modern architecture and organisational buy-in all have to come together.”
