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ROBOTIC PROCESS AUTOMATION
From back office to centre stage: the rise of RPA
Robotic process automation might not be the flashiest application of machine learning and analytics, but it has been steadily providing value for years. As AI becomes a hot topic in the industry, can RPA emerge from the shadows to greater recognition?
Senior Fintech Reporter Alex Hamilton
T
he fintech rollercoaster has no brakes. One moment a technology can be climbing that chain hill, the anticipation of its uses rising higher and higher, and then it will plummet
from the peak of hype into a swirling, corkscrew infested pit of disillusionment. Blockchain is already well on its way through these troubled waters and behind it, cresting the first great plunge, is artificial intelligence. Robotic process automation (RPA), as a key part of this growing sector, is often cited as the ‘workable’ face of AI technology, but that doesn’t mean it can’t fall prey to the same pitfalls.
“It certainly feels like we’re reaching the peak of the hype cycle in 2018. Any technology adoption takes a long time to permeate an industry, several years at least,” says Tom Doris, CEO of analytics firm OTAS Technologies. “The difference with AI vs Blockchain is that these techniques represent the latest versions of machine and statistical learning methods that have been used in financial services for many years. What we are seeing now is the realisation that providing AI represents a significant competitive advantage in the provision of scalable, high-quality services.”
Carmine Lengua Jr, market strategist at FactSet, adds that “regardless” of where AI stands on the hype roller-coaster today – whether interest waxes, wanes or continues to cycle in the future – ”what’s certain is that AI is less utilised today than it will be in the future”. He states that while 2018 might be seen as “the” year for AI, “then so will 2019 and 2020 after it”. When it comes down to it, “a lot of folks are talking about it, some claim to do it, and few know what it really is”.
Steve Mann, CMO of risk and compliance firm Arachnys, believes that 2018 might not be the year for AI, but that it won’t suffer “the
fate of blockchain” due to its “broad multi-industry application”. “We are confident that AI is in an innovation cycle where there are rapid advancements in both the technology and applicable use cases,” he adds. “However, this is a double-edged sword – as more and more use cases are experimented with, the more inflated the expectations of the technology become.”
Behind the scenes
AI, especially in the financial services industry, has yet to be properly segmented into defined sub-fields. However, RPA has the honour of being something of a front-runner to the AI and machine learning trends. “Most firms have some sort of RPA implementation,” says Mann. “Because robots can emulate human interaction, they can be used to gather and organise content, automate the management of bank transactions, perform analytics, or curate data into useable form.” AI, states Mann, is the natural evolution of RPA, not the other way around.
Not all RPA solutions deserve to be called AI, argues Lengua Jr. “Several firms today develop trade automation software that can diminish the burden of repetitive tasks borne by the typical buy-side trading desk. The simplest of these may employ pure rules-based automation, in which hard-coded parameters dictate whether – and how – a trade is routed automatically. This is RPA but should not be called AI. It represents little more than a macro in a spreadsheet.” How can one define an AI-led RPA solution? It’s “a system that learns from behavioural data, monitors market factors in real time, and adapts trading styles according to certain constraints, such as minimising impact or maximising alpha – this is an artificially intelligent form of
www.ibsintelligence.com | © IBS Intelligence 2018
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