Get in touch today
To see our real-time advanced analytics in action.
Automation at scale has, in recent history been closely associated with the manufacturing industry and in particular the impact it has had on blue-collar jobs. Robots replaced humans on assembly lines, reducing production costs, increasing productivity and improving quality. Until recently, computers have replaced relatively few white-collar jobs. Some jobs, such as typists, no longer exist, and in banks and exchanges, machines have reduced the number of traders. However, so-called knowledge workers, which have for some time been identified as a banks’ primary asset, have largely been immune. This will soon change. Jobs that involve data analysis, for example those in areas such as research and compliance, will soon become the domain of artificial intelligence. Intelligent machines will be to banks what robots were to the car manufacturers
What is artificial intelligence?
Artificial intelligence (AI) is the next big transition in computing and is fundamentally going to revolutionise the way banks operate. The technique, however, is not recent. Over the last sixty years there have been numerous significant advances under the radar in AI. The recent surge in interest – which has finally brought AI into the spot light – has been due to the dramatically decreasing cost of computing and the availability of exponentially increasing computing power. This, coupled with an explosion in stored data, has opened up the potential to automate tasks that have previously been considered the domain of humans.
Application areas for AI are widespread. For example understanding natural language, speech recognition, computer vision and heuristic classification. AI has already been applied in a wide range of fields including medical diagnosis, stock trading, robot control, law, remote sensing, scientific discovery and toys[1]. The greatest advances have been in computer vision where object recognition and tracking has already become commonplace and is powering advances such as self-driving cars.
The Boston Consulting Group (BCG Expand) has created a useful infographic that provides a taxonomy of artificial intelligence:
Where should banks be applying AI?
Relative to other industries, banks have been slow to harness AI. However, there are many functions today that are performed by humans such as: analysing information, making decisions or recommendations on what action to take that can easily be automated. Machines have the ability to absorb information from multiple sources and make decisions much faster than their human counterparts.
Functions such as research have always relied on human analysts to pore over company information and use experience and intuition to make recommendations. This is an obvious candidate for an intelligent machine and will allow investment research firms to cover many more companies and respond quickly to new information as it arrives.
Investment banking has many roles that have withstood the advancement of technology. Intelligent machines will be able to determine the optimal time and means for companies to raise capital; understand demand and select the right time for an initial public offering or when to issue debt and in which form. They will do this by pulling data from multiple sources including past issuance events and information from secondary markets far faster and more comprehensively than a human analyst could.
In the global markets, AI will breed a new generation of more sophisticated algorithmic trading, which is not simply about speed and hard-wired rules. In addition, similar to research recommendations, AI will also help to generate sales ideas and automatically disseminate these to customers at the most opportune moment. As banks develop their digital storefront, they will increasingly be delivered direct to clients over electronic channels. These could be, for example, an API or, increasingly, via chat, where the client is communicating with a bot rather than a human.
Whilst the most visible change will come in customer facing functions, it is likely that the first steps towards adopting AI will come from internal functions. There are many obvious applications in these areas, for example interpreting regulation and automatically translating these into rules that can then be used by machines to ensure compliance.
Other areas include operations, where many people that follow rules based workflow to ensure that trades are settled. Also, customer service functions will benefit from intelligent machines – which is already a target of many firms across many industries – to provide much improved customer support when there is a machine at the other end of the interaction.
What should banks be doing about this now?
As always, security and regulation are areas of concern for banks when adopting cutting-edge technology. With the scale of recent fines imposed on banks by regulators, they may understandably be less bold in flouting the rules in the name of technological advancement.
However, speed is imperative. Therefore, banks need to start partnering now with the right technology firms, from small specialist start-ups to big technology firms, to ensure they retain a competitive edge; maximize revenues and deliver a great customer experience.
The quickest route to success is partnering with innovative fintech firms to implement high quality solutions quickly and efficiently. In this case, banks may choose to buy in the technology rather than build in-house. This will allow them to keep pace with their competition and quickly apply AI using the expertise and investments made by the early movers.
Clearly, this is such an important area that banks cannot risk doing nothing. Without AI they will struggle to compete on cost and efficiency and the challenges will likely hit their most profitable and lowest risk businesses.
Banks must act now to develop partnerships with technology and data specialists and position themselves at the forefront of the next generation of financial services.
By Diane Castelino Ph.D,
Data Science And Research Lead, Mosaic Smart Data
IN THE NEWS
Insights
