Data has gained a new prominence in modern society. The constant flow of news, incidents, investigations, public debate, regulation and law making has raised awareness of how business, government and individual's data can be used and abused.

The meteoric rise in social media's global adoption and influence has acted both as a stimulus to new ways of handling personal data and an uncomfortable example of how dramatically these can compromise personal privacy and impact commercial interests, for good or ill. In business, major advances in data analytics capabilities, tools and economics are reflected in a growing willingness to embrace hybrid cloud, grow increasingly skilled data science practices and act to shift culture decisively towards technologically-enabled intelligence. 

At its most successful, where data used to be produced as a by-product of running a business, now business is produced by the intelligent application of analytical techniques to its data. This is nowhere better demonstrated than in today's financial services industries whose core activity is handling data and whose reputation rests upon on complete trust, absolute data integrity and privacy, whether they operate in banking, insurance or financial markets.

In this newly data-driven world, what role does the emerging use of artificial Intelligence play? How can it be put to work for socially and commercially productive purposes to improve lives and businesses? And what is artificial intelligence anyway? 

Artificial Intelligence defined

AI can be defined as intelligence exhibited by machines. In computer science, the field
of AI research defines itself as the study of intelligent agents, that is, any device that
perceives its environment and takes actions that maximise its chance of success at
some goal. Colloquially, the term 'artificial intelligence' is applied when a machine
mimics cognitive functions that humans associate with other human minds, such as
learning and problem solving.

AI is not new. The drive to create the capability of machines to simulate aspects of human intelligence has been envisioned for millennia and AI-focused scientific and technological research pursued in the modern era for at least 60 years¹. The rapid emergence of AI-infused tools in customer service contexts, such as intelligent telephone agents, avatars and so called 'chatbots' is mirrored in intelligent supply chain management applications, assisted domestic energy management, fitness management applications, medical diagnostic applications and a thousand other use cases, down to the self-correcting spelling, prompted words and selection of language on our personal smartphones.

Neither is artificial intelligence 'fake' intelligence. It is rather machine-executed processing which simulates certain attributes of human thinking, for example handling and making sense of multiple sources of structured and unstructured data. Whilst the machine will be programmed to achieve some goal, the way in which it will act will vary dependent on what kind of analysis is called for. At its most advanced this will include 'unsupervised learning', where the machine will itself make sense of many data inputs, then act on that knowledge to augment its understanding, driven all the time by the goal it has been set to achieve. 

Good governance for the right outcomes

It follows that the quality of the governance, management and oversight of analytical activities which rely on artificial intelligence are extremely important to get right, whether the application of the intelligence be in the social, governmental, military or commercial domains.

This recognises that in the right hands, with strong and well-exercised governance, with well-formed goals informed by expert data scientists, artificial intelligence holds great potential to improve lives, economic efficiency, improve healthcare and prevent financial crime, to name but a few of the key benefits now within reach.

To do so to best advantage, a key enabler and accelerator will be either the establishment of a capable in-house data analytics practice or, where the skills are not available, procuring the services of expert partners who have those skills. 

Why AI matters to Financial Services now

In Growing the artificial intelligence industry in the UK (April 2018) Professor Dame Wendy Hall and Jérôme Pesenti claim that 'AI could add an additional USD $814 billion (£630bn) to the UK economy by 2035, increasing the annual growth rate of GVA from 2.5 to 3.9%'.

That sounds like a goal well worth pursuing.

Turning to AI in financial services, AI deserves our attention for two big reasons:

Better business outcomes

Revenue growth, cost reduction, operational efficiency improvement, risk and fraud reduction, customer satisfaction and loyalty improvement. Each of these business outcomes is available to the financial services organisation which can define the issue or opportunity to be tackled accurately; to recognise what additive value AI will bring to its analytics and operations and to organise itself to put the right systems, controls, expertise and sponsorship in place. For those who do this, the investment will create business outcomeswhich stand them apart from their competitors.

Integrated change

Artificial intelligence disciplines sit at the heart of a set of linked technology changes which are in the process of reshaping what work is done and how it is enabled. They are also redefining how data is gathered, how used and with what safeguards. For a successful

introduction of AI into financial services organisations, it is therefore important that the interdependent parts of the system be recognised and addressed by the financial services organisation as it reshapes itself, through artificial intelligence, for greater success.

Starting well: business-driven thinking

AI serves the business so the business must first work out why it would want AI. AI can and should augment data analytics on which financial services businesses are now fully engaged, so applying AI can be a natural enhancement of what is already going on.

It follows that the pathways can be quite similar, although the technologies deployed may be quite different.

AI can offer many benefits to help tackle the main challenges financial institutions face today. All four main areas of attention that the industry now faces, in customer experience, trust and compliance, business reinvention and operational excellence, highlight specific issues or opportunities where one of the AI technologies can offer value. 

But, as there is no shortage of use cases that can apply AI in financial services, it can appear daunting to know where to start. One way is to consider the ways in which AI could better fulfil an opportunity or better solve a problem than existing approaches have been able to achieve. Another way is to consider new opportunities which are as yet unaddressed and for which AI might provide a unique benefit. 

Whichever is chosen, developing a well-formed use case is a critically important task which requires inputs from many sources other than data scientists. The knowledge contributed by subject matter experts, process practitioners, customer facing staff, legal, regulatory, risk and compliance professionals, customers, analysts and consultants will need to be sifted and considered carefully to conceive both the unique value-add to be created by AI, and the solution framework which it would deliver it.

Artificial intelligence is here to stay and its possibilities are only now starting to come into focus. Progressive financial services organisations which recognise, act and learn fast on AI will be the ones who reap rewards and remodel their organisation, performance and customer relationships.

It's an exciting prospect.