While artificial intelligence is transforming several industries, the financial sector has a lot to learn from specific case studies in non-banking areas such as health, travel and retail. To start at the beginning, despite the term AI having been bandied about with a number of different definitions, the actual definition is simple: technology that appears intelligent.
Having been in existence since the 1950s, early forms of the technology were created and designed to mimic human nature, and this is where the controversy and misconceptions have emerged from. After a period of quiet development, artificial intelligence has undergone a modern revolution with subsequent excitement as a result of techniques such as machine learning coming to the fore.
Abhijit Akerkar, head of AI business integration at Lloyds Banking Group, reveals that a bank’s journey into artificial intelligence starts with experimentation. For Lloyds, it was around 24 months ago. Experimentation provides a low-cost way to test what works and what doesn’t. This experience shapes the portfolio of use cases and hence, the trajectory of value creation.
“The last three to four years have seen the explosion of data, easy and low-cost access to powerful compute power, and availability of sophisticated machine learning algorithms. The stars are aligned for the breakthrough. No wonder, companies have stepped up their investments towards embracing AI,” Akerkar says.
Machine learning is another term that has been bandied about and it must be remembered that this is a subset of AI; all machine learning is AI, but not all AI is machine learning. Machine learning enables programmes to learn through training, instead of being programmed with rules and as a result, can improve with experience.
This is why there has been excitement about machine learning in financial services, as MMC Ventures explored in their report ‘The State of AI: Divergence,’ in partnership with Barclays. “Machine learning can be applied to a wide variety of prediction and optimisation challenges, from determining the probability of a credit card transaction being fraudulent to predicting when an industrial asset is likely to fail.”
Deep learning, which is a subset of machine learning, has also become mainstream knowledge because of how it emulates the way animal brains learn tasks, but deep learning has not been used to its full potential in financial services, yet.
The tipping point
MMC Ventures also state that after “seven false dawns since its inception in 1956, AI technology has come of age. The capabilities of AI systems have reached a tipping point due to the confluence of seven factors: new algorithms; the availability of training data; specialised hardware; cloud AI services; open source software resources; greater investment; and increased interest.”
In what is being described as the fastest paradigm shift in the history of technology, banks can now adopt AI technology because of the shift to cloud computing, offerings from vendors and software suppliers. According to Gartner, only 4% of enterprises had adopted AI in 2018. Today, this figure has jumped to 14% and a further 23% intend to deploy AI within the next 12 months.
“By the end of 2019, over a third of enterprises will have deployed AI. Adoption of AI has progressed extremely rapidly from innovators and early adopters to the early majority. By the end of 2019 AI will have ‘crossed the chasm’, from visionaries to pragmatists, at exceptional pace - with profound implications for companies, consumers and society,” the MMC Ventures report posed.
But is this interest all conflated hype? MMC Ventures also revealed in March 2019 that 40% of Europe’s AI startups do not use any AI programmes in their products, as was reported in Financial Times.
Based on interviews and investigation into 2,830 AI startups in Europe, David Kelnar, MMC’s head of research, said that while many of these firms had plans develop machine learning programmes, none actually were at present. “A lot of venture capital groups in Europe are responsive to companies that are interested in raising money [for AI],” Kelnar said.
The FT went on to report that companies that are branded as “AI businesses” have historically raised larger funding rounds and secured higher valuations in comparison to other software businesses. In addition to this, politicians have also contributed to this hype by discussing so-called AI success stories.
At Finextra’s annual NextGen Banking conference, keynote speaker head of AI at TSB Bank Janet Adams framed the debate and stated that “AI is the new electricity” and has the potential to power everything we do in the future, helping banking customers thought the wealth creation stage of their lives.
However, despite hype around uncovering the mysteries that surround the technology, Adams pointed out that business models cannot succeed without proper education of staff in financial services, and only then, strategic advantage can be gained. “Data equals training equals insight. Roshan Rohatgi, AI lead at RBS, agreed and added that “everyone is keen to use this stuff, but the system, the fabric, is not mature yet. It’s all well and good to go from POC to pilot, but it never really reaches the real world.”
The hype discussion continued in Karan Jain, head of technology Europe and Americas from Westpac’s keynote, in which he explained that a lot of discussion about AI is around FOMO - fear of missing out. And this “FOMO generation” have different expectations and “want their banking services to be available in a couple of clicks.”
It was also argued in a later panel discussion that this FOMO also exists within the corporate banking infrastructure, where the board may ask executives if they are working with artificial intelligence - after having heard about the technology in the news - for the execs to reply, revealing that their bank has been using machine learning for a few years.
In conversation with Finextra, Prag Sharma, head of Emerging Technology, TTS Global Innovation Lab, Citibank, highlights that there has been a recent resurgence in artificial intelligence and this is because of the development in the overall capability of the technology driven by “data, processing power, cost and algorithms, products and services developed by the open source community.”
Annerie Vreugdenhil, chief innovation officer at ING Wholesale Bank suggests that AI is already part of our everyday lives and is more prevalent than first thought. “The world is changing rapidly through technological developments and as a result, our expectations are changing. As we adapt, and these technologies become more intertwined into our lives, our expectations around what could be achieved also grows. We believe in stepping out of our comfort zone, even beyond banking, to explore the opportunities, and as we do this, our expectations extend beyond further than we have ever imagined before.”
Paul Hollands, chief operating officer for data and analytics at NatWest has a different view. After saying that he was “a terrible person to ask whether AI is a buzzword or not,” he said he has always thought that AI was “a massively overhyped term. It is a collection of capabilities, so you know, if you think about it in its simplest form, it’s machine learning, its robotics and it is to some extent, chatbots as well and I think a lot of what we’re trying to do is around how do we used advanced techniques to help get to smarter outcomes for customers.
“We’ve been using machine learning for a long time in terms of how we identify opportunities to customers to save money and do things differently. I sit there and think machine learning isn’t that new but the technology that is available that put the data through and true and a speed at which it is palatable enough to get an answer - that is new.”
Machine learning lead at Monzo Neal Lathia adds that “machine learning is well beyond the peak of inflated expectations, but the broader usage of the phrase ‘artificial intelligence’ is hyped to a cringeworthy degree.” OakNorth’s chief operating officer Amir Nooralia had a similar view and said that while there is hype around AI, he believes that it is justified and not just part of a cycle.
“The hype is here to stay and if anything, will only continue to grow over time as more use cases develop and more propositions are proven. Personally, I think the tipping point will be commericalised AI: moving away from AI chatbots which make us more efficient to AI making commercial insights that lead to more profitable businesses. Once that is proven in an industry, it will permeate across quickly and then replicated across other sectors. We saw this with investment banking and algo-trading and how quickly it took off, once money was being made.”
Stephen Browning, challenge director - next generation services at Innovate UK, provides a concise outlook at artificial intelligence and explains that “it is not necessarily the technology that’s important, it’s the projects and programs in which AI is getting used. What we’re seeing right now is a surge in interest around a particular type of AI that is machine learning and variants of that such as deep learning and that’s driven substantially by two main things that have developed and come along.
“One is the computing power available at a reasonable price and the other is the availability of large quantities of data. When you bring those two together you have the ability to use machine learning models to do some things that are quite remarkable in terms of the ability to spot patterns, but it’s not intelligent in the normal sense of the word.
“These techniques come under the broad title of artificial intelligence, that’s really why there is a surge in interest at the moment. The opportunity to apply these techniques to new areas where there is access to data gives the ability to spot things that maybe you couldn’t spot before so when you’re talking about financial services, identifying fraudulent transactions far more readily or using machine intelligence to assess communication and potentially see where people aren’t being so honest and spot fraud.”
Finextra's The Future of Artificial Intelligence 2019 report explores how the financial services industry can leverage tried and tested experiments of AI in other industries to transform how transaction services can be reshaped.