Is the buy-side too slow to adopt AI?
Posted: 7 November 2017 | Marianne Brown - Chief Operating Officer at Institutional and Wholesale at FIS | No comments yet
Artificial Intelligence (AI) is making waves across a range of sectors. It’s also making trades: an increasing number of quant funds, for example, are now directed solely by AI-powered trading engines. For now, they may be the exception rather than the norm, but few would deny that we are in a world of rapidly shifting competitive dynamics.
On the buy-side, asset managers face increased pressure to offer high value, low margin solutions for their clients. Yet many heavyweight buy-side players still pack an underpowered punch when it comes to infrastructure and technology adoption that can support the investment research and operational efficiencies they increasingly need – as our recent Readiness Report shows.
The opportunities afforded by machine learning, robotic process automation and other forms of AI are immense and evolving fast. Market actors are extracting sentiment from social media and transforming it into actionable indicators; they are algorithmically parsing relationships between events and asset prices that few would previously have linked and, above all, increasingly automating investment management activities, including portfolio construction.
Those that fail to keep pace will lose competitive edge; there is little doubt about it.
Desktops from the ‘80s?
Big hair, big suits, big returns and big desktop solutions; call them the Big Four of the 1980s. While the first three might now be considered exotic oddities, the latter remains surprisingly widespread, particularly across the buy-side.
Recognition is growing, however, that – as the balance of power in the market shifts (one recent Bloomberg report suggested that “the dealer has essentially been demoted from maître d’ to a waiter taking orders”) – tech investment is vital.
Industry concerns are widespread, as our survey of 1,000 senior executives across the buy-side and sell side shows: 75% of executives feel they don’t have the technology capability to support their firms’ growth ambitions. Many of them continue to rely on inefficient manual routes for regulatory compliance.
Intelligent and future-fit platforms
Others are stepping up to the plate and recognising the opportunity, including UBS Fund Services, which is using FIS’ Investran Insight to provide their investment managers and investors with interactive reporting, advanced analytics, and data visualisation across its private equity value chain.
The Investran Insight tool utilises a portal experience that integrates with the UBS client web portal and provides intuitive reporting with detailed information on investors, funds and underlying portfolio companies that significantly reduces manual intervention and operational risk. A UBS Fund Services Director of Product Deployment describes these capabilities as “a clear differentiator” for his private equity and infrastructure clients.
Our research shows that buy-side firms – though starting from a lower level of automation – are shifting gears and expect to make larger strides than sell-side firms over the next three years. In trade execution and reconciliation, buy-side firms expect to reach a position where 59% and 58% of these processes respectively, are near-fully or fully automated. They also anticipate a significant leap with respect to exception management.
(For sell-side firms, there will be a steady increase in automation levels of trading and post-trade processing, and a more notable jump in collateral management.)
Automatic for the people
Full service automated solutions often prove to be money well spent. In the event of any counterparty default or market crisis, any system must provide a clear view of all margin calls and pledges to all bilateral counterparties in one place. This in addition to visibility of allocated and unencumbered collateral across all funds; substitution and pledging and more.
But technology improvements aren’t just about keeping compliance happy: across market sectors technology and data are opening up possibilities that can help asset managers adapt to a changing environment. A paradigm shift in AI – including rapid advances in robotic process automation and emerging forms of machine learning – is allowing firms to rethink existing models for post-trade support and better understand how and where recent innovations, such as utilities, fit in the overall post-trade ecosystem.
The emergence of serviced utilities
The emergence of multi-tenant operating models as the most logical and economical vehicles for delivering advanced technology into the post-trade environment, is also helping to increase efficiency and reduce risk in areas such as reconciliations, margin processing, exception management and technology support. This is creating compelling economies of scale and incentives to implement advanced technologies.
Credit Suisse, for example, is now using FIS’s Derivatives Utility for its post-trade futures and cleared over-the-counter (OTC) derivatives operations and technology.
The utility provides derivatives clearing operations and technology services for trade clearing, trade lifecycle management, margin processing, brokerage, reconciliation and data management – each at a level of sophistication that, quite likely, may not have been able to be supported in-house, owing to capital pressures.
In short, by leveraging economies of scale in middle- and back-office processing and technology, market actors such as futures commission merchants (FCMs) can radically boost efficiency.
For buy-side funds like pension funds – a traditionally risk-averse sector – meanwhile, it’s not surprising that conservative views about technology adoption sometimes still prevail.
Yet as the complexity and regulatory pressures of asset management accelerate (amid a backdrop in which the search for yield remains extremely challenging) the need to take a more proactive view to technology adoption is clear.
As margins shrink, the productivity of the buy-side’s key resource – the investment professional – becomes even more critical. The more data, analytics and technology they can access, whilst automating away time-sapping and low-reward compliance issues, the better. It’s time to take the robot out of the human and let creativity flourish.