According to new research from JP Morgan, while funds are stepping up their integration of AI, adoption remains measured due to the sector’s stringent regulatory environment.
AI is projected to contribute $15.7 trillion to the global economy by 2030, revolutionising business practices, according to PwC.
As part of JP Morgan’s research, State Super CEO John Livanas emphasised the transformative impact of machine learning on the fund’s investment management and operations.
“AI (machine learning) has helped our organisation interpret massive amounts of market data into possible market signals for our investment team,” Livanas said.
“Given that our funds are in severe negative cash flow, we don’t have the luxury of allowing the market cycles to play out. Our machine learning capabilities allow our investment team to consider these signals as part of their market assessment and respond more rapidly to downside surprises.”
Livanas also detailed the fund’s exploration of AI-powered large language models to manage complex defined benefit schemes.
“This is intended to provide relevant support to our customer service team when they are responding to complex member queries,” he said.
The fund is also rolling out Microsoft Copilot inside the organisation to speed up the pace of retrieving data and policies.
“As we get into a more highly regulated environment, we need to be able to interpret regulations and policies in our operations,” Livanas said.
Other funds are also experimenting with AI.
Namely, Jo Brennan, group executive, member engagement, education and advice at Aware Super, revealed that the fund is set to trial chatbots capable of swiftly answering questions and summarising information.
“Some customers have told us they prefer using a chatbot initially because they can start asking basic questions to start their learning journey without feeling judged,” Brennan said.
AustralianSuper, too, is training its staff to use AI across various functions, including investment analysis, document management, and marketing. However, the fund remains focused on back-office applications rather than member-facing services.
“Our primary focus on AI at the moment is in the back office and corporate services. This includes machine learning modules, generative AI and automated processes,” Mike Backeberg, chief technology officer, AustralianSuper said.
Meanwhile, the government has proposed 10 mandatory guardrails to ensure AI’s safe and responsible use, complementing existing prudential standards CPS 234 and CPG 235, which govern information security and data risk management.
As funds explore AI’s potential, innovations like chatbots, Microsoft Copilot and advanced automation tools are poised to enhance both operational efficiency and member engagement in the years ahead.
AI reshaping asset management
A recent Mercer survey found that AI is broadly reshaping the asset management industry, with a majority of asset managers already leveraging or planning to integrate the technology.
While AI adoption has surged over the past year, Mercer noted this growth is the result of three years of behind-the-scenes development.
“Today’s players are in an arms’ race to deliver next-generation, game-changing technological solutions,” said Nick White, global strategic research director at Mercer, during a recent webinar.
“Every CEO has replaced the word ‘technology’ with the word ‘AI’ because they all want to be doing more there and be seen to be doing more.”
The AI Integration in Investment Management 2024 survey, which polled 150 asset managers, revealed that 90 per cent are actively using or plan to incorporate AI into their investment processes.
Current usage primarily focuses on enhancing data analysis and idea generation, while a smaller group employs AI for portfolio construction, asset allocation and rebalancing.
“Machine learning is predominantly used to improve portfolio implementation efficiency rather than to predict returns,” one respondent said.
More than half of AI-integrated teams reported using AI insights to inform – rather than dictate – investment decisions, with 20 per cent noting that AI proposes decisions that human managers can override.
Despite its promise, Mercer emphasised that AI’s commercial impact on assets under management and revenue remains uncertain.
White underscored the need for asset managers to build AI expertise, as the technology continues to disrupt industries.
“The gap between leaders and laggards will naturally be greatest in the industries where AI is causing the most disruption,” he said.
