Many financial institutions and service providers remain encumbered by creaking technology systems that are preventing many from taking advantage of artificial intelligence (AI) data innovations.
Despite organisations’ overwhelming desire to make use of AI to give them a competitive edge, many say also that they lack the data management expertise to adopt applications that are changing the way institutions do business.
The mixed picture of financial firms’ technological readiness was revealed in a slew of surveys released in recent weeks. Together they paint a picture of an industry yearning to take digitalisation’s next step, but being hamstrung by poor infrastructure and data policies.The revelations come as institutions seek to find new efficiencies and create value from the huge amounts of data that they are generating from their own activities and buying in to help them meet regulatory compliance obligations and empower their workflows.
AI is viewed as a game-changing technology that can not only find and present insights from huge datasets, but also fuel new portfolio and risk management models, streamline front-to-back-office operations and aid product development and sales teams. That’s become even more critical as investment and reporting teams must deal with a rising number of use cases for their data, including ESG compliance, and new investment theses that include entry into novel business areas, such as private, wealth and digital markets.
Enhancing the Future
Institutions’ appetite for new AI-linked technologies was highlighted in a survey by Swiss markets and data provider SIX. Its study, conducted in conjunction with Coalition Greenwich – an S&P Global benchmarking and analytics provider – found that three-quarters of the 67 buy-side and sell-side firms it questioned said that AI, including generative AI (GenAI), and machine learning (ML) would be important in enhancing future market data delivery and consumption.
“Generating better investment decisions stands out as the top consideration for employing AI and ML,” the study stated. “Participants value generative AI/large language models the most when developing investment ideas. Nearly half of participants agree AI/ML will be used for intelligent automation of business processes at some point, but the most exciting application remains better recommendations.”
Nevertheless, respondents offered a duality of opinions, with two-thirds saying that cloud integration would be the key driver of market data optimisation. AI and ML also raised security concerns among respondents, with erroneous data generation, data leakage and implementation costs topping the lists of worries.
“High-quality data is paramount to any type of data use, no matter how transformative it is,” said David Easthope, senior analyst for Coalition Greenwich Market Structure & Technology and co-author of the paper.
“The use of generative AI by market data users is becoming increasingly apparent — especially for creating and supporting investment decision-making. This development is underscored by the shift toward higher frequencies of data and more efficient data delivery,” Easthope added in the statement.
Management Shortfalls
C-suite individuals in the US surveyed for ActiveOps, an AI-focussed advisory, reported discrepancies between financial companies’ technological ambitions and their capabilities. The study, conducted by Censuswide, found that three-quarters of the 850 chief operating and financial officers, as well as senior heads of operations interviewed, said they thought AI would enable better decision making.
Even so, two-thirds also said they lacked the data management capabilities to take advantage of the potential the technology offers. It found that about half couldn’t access real-time data and two-thirds had legacy infrastructures that were unsuited to an AI transformation.
The need for new capabilities is apparent, the survey found: about four-fifths said they can’t derive insights from their data without “significant effort” and a quarter said that they are having to make crucial business decisions on data that is as much as two weeks old.
“These findings clearly demonstrate a willingness to embrace AI among financial services operations leaders in the US,” said Spencer O’Leary, chief executive in North America at ActiveOps. “There is a long road ahead, even for early adopters, and there is a real risk that bad data could leave some behind the AI curve. AI will change the game, but only if we play by the right set of rules.”
Outdated Practices
A third survey, which sought to quantify the cost to capital market firms of relying on outdated data management and sharing technology, found that most respondents were still using spreadsheets for their bulk trading and post-trade reports.
The report, published by capital markets technology provider Axoni and Coalition Greenwich, also found that four-fifths of respondents were bogged down by the surge in data volumes as it hit inadequate extract, transfer and load solutions.
It said that the cost to firms ran into millions of dollars every year.
“For some capital markets firms, manual data reconciliation adds millions of dollars in expenses every year,” said Audrey Costabile, senior analyst for Coalition Greenwich Market Structure & Technology and author of the report entitled “Operations Data-Sharing: A Critical Time to Innovate”.
“Across the industry, firms must embrace innovative data tools if they are to meet changing regulatory and operational demands while also ensuring data privacy, accuracy and efficiency,” Costabile added in a statement.
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