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Behind The Numbers – Why Financial Services Spending on AI is Set To Soar

It’s an image that has come to symbolize financial markets. Whenever there’s a sudden dip in shares, an unexpected hike in bond yields or a run on a major currency, the TV news coverage will cut to footage of a trading floor stacked with computer screens. It’s a shorthand way of illustrating the importance of real-time data to an industry where fortunes depend on traders making the right call.

It’s been that way since the mid-nineteen eighties, of course, but what we are seeing at the moment is a step-change in the way that digital information is being used within the financial markets sector. The roll-out of ever-more sophisticated Artificial Intelligence (A.I.) and Machine Learning (ML) tools is enabling businesses to cut costs, make better trading decisions, become responsive to customers and – just possibly – create a more secure trading environment. Or to put it another way, the trading room screens may not look totally dissimilar to those we’ve been looking at for the last four decades, but financial markets professionals are tapping into a much richer well of data and their work is increasingly being underpinned by new analytics and process automation applications.

Or at least that’s the direction of travel. According to research by consultancy firm Deloitte, 84 per cent of major banks and financial services executives say that A.I. will be of critical importance to their continuing business success. Breaking that down, 47 per cent of respondents cited cost reduction as a priority and 67 per cent said they were targeting enhanced revenues. In addition, 47 per cent saw opportunities to improve customer engagement.

Meanwhile, spending on A.I. and machine learning related projects is expected to rise sharply, from around $5.6 billion in 2019 to $19 billion in 2025.

A Profound Impact

This level of spending will have – and is already having –  a profound impact on the industry, not least in terms of staffing numbers. Consultancy firm Opimas estimates that more than 400,000 jobs will be lost in and around the financial markets by 2030 as a result of the new technology, with asset management feeling the biggest impact. Clearly, that feeds into the cost-cutting agenda of many businesses but that’s certainly not the whole picture. A.I., as deployed in functions such as asset management, is already enabling businesses to optimise their decision making and trading performance.

Essentially, technology has become a key differentiator within the industry, enabling the most-competitive firms to carry out thousands of trades per second. The key to improved performance lies in running data from multiple sources through algorithms and analytics tools. Today’s systems identify trends and market movements while also making the kind of connections that would be impossible that would be missed by human eyes. The ability to crunch numbers across hundreds or thousands of data points means that trading is based on the most accurate -and three dimensional –  reading possible of the events that drive markets.

Applications  

So what does this mean in practice? Well in terms of enhancing performance, A.I. is being deployed to drive performance across functions such as portfolio management, capital optimisation and forecasting.

Equally,  A.I. is helping businesses to manage risk across their operations – for instance by identifying patterns that could indicate fraud or criminal activity. Importantly – in terms of efficiency – machine learning drives a greater degree of accuracy when potential issues are flagged up – for instance, over time, the systems become adept at identifying false positives.

Meanwhile, on the customer engagement side of the value chain, we are already seeing big changes, not least in the form of chatbots and robo-advisors. These A.I. and machine learning-enabled tools, probably won’t replace human interaction entirely but what they will do is provide customers and clients with an efficient means to access advice and information on a 24/7 basis.

Again, this could be seen merely as a cost-cutting measure, but there is a bigger opportunity. As systems get smarter, financial markets companies are empowered to provide services that are not only automated to a high degree but also personalised and customised to the needs of individuals. In other words, there is an opportunity to provide a much more responsive service while keeping costs in check.

There is, a genuine revolution taking place across the financial markets and the effect is already becoming visible. Witness Goldman Sachs, which has reduced the number of traders in its equity trading division from 600 to two in less than a decade. But we are still at the beginning of a journey. Firms must decide where and how to prioritise their A.I. spend in line with their objectives. And while there are opportunities, there are also risks in terms of security, project drift, systems integration and quality of data. I’ll be taking a closer look at the risks in my next article.