The same applies to businesses. And for firms operating in capital markets, the signals for change reside in data – not market data, but transactional data. The vast bedrock of information all banks sit on, but most of it can remain untapped, with its potential yet to be realised.

However, an increasing number of banks are realising that AI – if applied correctly, after the appropriate groundwork – is the technology that holds the key to unlocking this potential. And this evolution is not unique to capital markets – research firm IDC has predicted that the global AI market will swell to be worth more than $500bn this year. As we head into 2023 amidst the backdrop of volatility and economic uncertainty across the globe, these are the top five emerging trends in AI that are helping banks achieve a competitive advantage.

1. Liberating precious time and resources

One of the first benefits a bank can realise from AI is freeing up its human capital to focus on nuanced and valuable work, where employees’ skillsets are fully leveraged rather than being tied up in basic, administrative tasks.

A core use case for AI is the automation of repetitive business processes that can take up hours or days of an employee’s time each week. This can immediately transform the productivity and level of client service a bank is able to provide.

A common complaint among data scientists is that they spend most of their time in data wrangling rather than actual productive analysis. If all transaction data is accessible by data scientists and quants in a single consistent format, with AI applied to perform repetitive tasks automatically, then this challenge is virtually eliminated.

2. Increased sophistication with shorter time to market

Another benefit banks are increasingly seeing from AI is that, while the tools are getting more and more sophisticated, the time to market for deploying them is getting shorter and shorter.

Agile, forward thinking data analytics specialists are able to innovate quickly, and work with banks in a collaborative manner to help them get up and running with greater efficiency. For example, we partnered with the European Space Agency back in 2018 to explore how AI algorithms developed for use in space could be adapted and deployed in capital markets. Fast forward to today, and this technology is in real-world deployment in live capital markets by a number of our customers who were able to benefit instantly from this cutting edge technology that originated in a completely different field.

3. Personalisation at the individual level, rather than the segment

In a world where we increasingly expect personalisation of digital services in our consumer lives – for example recommendations by Netflix on which movies we might enjoy – clients are also demanding greater personalisation in the enterprise space.

Whereas banks typically looked at their clients by segment and tailored the information they provided to them in this manner, they are now beginning to use AI to look at each client as their own segment, and hyper-personalising the insights and service they provide them. This is optimised with natural language generation technology, which delivers reports such as multi-asset morning briefings in a human tone of voice with easy-to-interpret analytics. The result for the bank is increased loyalty and greater share of mind amongst clients.

4. Fusion of the human and the database

As man and machine increasingly work in tandem, banks continue to benefit from the fusion of the human and the database. Whereas before, a bank that wanted to leverage its data to obtain insights on its clients’ trading behaviour would have had to structure a query, AI can now be used to perform a natural query – simply asking questions like “who is my best client?”, or “which client needs more attention today” can deliver the answers required to take positive action.

Think of it like Amazon’s Alexa for capital markets. And the tools are sophisticated enough to query all types of data – trade records, news, market data, the list goes on – to provide a qualitative, holistic response.

5. The ability to understand – and explain – data models

Model explainability will continue to become increasingly important in the field of data analytics in 2023. Banks want to know exactly why they are being given certain intelligence, and how the models underlying AI technology are working. As a result, their workforce is becoming more fluent in generating and calibrating models to meet their specific needs – and vendors are increasingly working in collaboration with them, rather than the typical vendor-customer model.

Analytics vendors have come to the realisation that the time at which they deliver their AI product to a bank is not binary – instead, they can allow customers to develop their own models that sit next to theirs, delivering the best outcome for the bank. For this approach to be successful, it’s critical the vendor has specialist knowledge of the specific capital markets they sell into.

Becoming data-first

Banks have always had an advantage when it comes to data, and by leveraging AI appropriately they can ensure they retain this advantage as capital markets continue to modernise and become more data-centric. As these five trends show, the most precious resource for a bank is its transaction records – and within this is its guide to where opportunity resides. Extracting the value from these records is no longer a nice to have, it’s a core necessity for competitive advantage.

Those that take action now will benefit from being the first past the post – they will generate more business and start to generate more data, from which they can extract more insight and continue the cycle of prosperity. As we head into 2023, the race to become data-first and deploy AI effectively is well and truly underway.