Lack of AI and machine learning adoption risks some banks falling behind

Given the critical role that banking and financial services play in the British economy, it is reasonable to think that it would lead the way in adopting new technology that increases efficiency, insights and security. 

To some extent, this is the case. Financial services firms have started integrating both machine learning (ML) and artificial intelligence (AI) into their operations – building basic, but coherent, digital strategies that help ensure regulatory alignment and generate internal efficiencies.

However, progress has been slower than expected and gaps still remain, particularly in customer service and data analysis. This is despite the emergence of a number of innovative new players like Monzo and Starling Bank that have pushed the technological envelope. 

These businesses carved out a niche in the market by using new technology to offer insights that improve the customer experience, ultimately demonstrating the power of a digital-first approach integrated with AI and ML. 

Traditional banks and financial institutions risk being left behind as consumers turn to these new, more agile competitors if they neglect the growing demand for a banking model that integrates these innovations to improve the banking experience for consumers. 

To explore how this next stage of digital transformation can be achieved we conducted our own research on the sector’s use of technology. Our final report identified the key deficiencies in the traditional industry players’ implementation of AI and ML, the risk that this poses to their success, and how this could be rectified. 

Structuring unstructured data

The most significant area of untapped potential is found in the use of data. Financial institutions are extremely data-rich organisations and customers often remain with their chosen supplier for a long time meaning, that they can collect years’ worth of information on their habits. 

During this time, it’s also likely that customers will be in frequent communication with their bank through multiple channels, whether through a high street branch or an app, which adds depth of insight and understanding. 

Once collected by an organisation, data is categorised as either structured or unstructured. Structured data includes basic information like names, addresses and credit card numbers. 

However, our own research demonstrates that it’s in the analysis of unstructured data that the sector is falling short. Unstructured data includes audio, video and email files and, crucially, makes up around 80 per cent of the data that banks hold. Only three per cent of organisations that we spoke to are properly evaluating unstructured data.

Integrating AI and ML to analyse unstructured data is an opportunity that could transform banks and their approach to improving customer experiences and being more efficient. 

For example, by analysing unstructured data with ML banks can uncover patterns in their interactions with customers, allowing them to proactively address problems before they appear and tailor their approach on a customer-by-customer basis. 

In practice, this could allow banks to gain a deep enough understanding of customer service conversations to understand what customers respond well to, and the warning signs that appear when a customer is on the brink of switching to a competitor, meaning they can take action to tailor their approach and rectify the situation. 

Overcoming roadblocks

As with any transformative innovation, there are still a number of roadblocks that act as buffers against change though. The most significant of these are legacy IT systems. AI and ML are easier to adopt when cloud technology is used to store data centrally, so older systems that lack this capability impede change. 

Research has found that 92 of the world’s top 100 banks still rely on outdated systems – an issue that must be addressed quickly. IT systems should be viewed as a key part of business strategy rather than a support function. In an industry renowned for more traditional approaches, this will require a level of culture change. 

It’s important that industry leaders take the initiative to build their business around technology rather than falling back on approaches that they’re more familiar with. By embracing digital transformation and empowering IT teams to lead on the implementation of innovative new technology like AI and ML, it’s possible to reimagine what banks can offer to customers and how agile they can be when adapting to new demands and shifting markets. 

Banking is changing and those that do not adapt run the risk of being outflanked by the competition. A full digital transformation that integrates AI and ML in the analysis of structured and unstructured data is fundamental for any institutions that wish to maintain their status as industry leaders. 

Ryan Stewart is financial services lead at Cloud Technology Solutions.

The views and opinions expressed in this Viewpoint article are solely those of the author(s) and do not reflect the views and opinions of Fintech Bulletin.