Unlocking the benefits of unstructured data in banking
The banking sector’s digital transformation journey has come a long way over the past five years. Today, technologies like machine learning (ML) and artificial intelligence (AI) are becoming much more commonplace in banks’ digital strategies.
But the sector wasn’t always as open to embracing new technologies. Until the arrival of challenger banks like Monzo and Starling Bank a little over five years ago, who showed the benefits that taking a digital-first approach could bring, traditional banks failed to adopt the digital transformation at the same speed as other sectors.
While the banking sector has come a long way in its digital journey in recent years, the slow pace of adoption initially has had a knock-on effect on the sector, with other industries now far more advanced in their digital approach. Take healthcare, for example. The industry was an early adopter of nascent technologies and as a result, it is now using ML to help with early detection of diseases like cancer.
We conducted our own research into the banking industry’s digital journey, to see where technologies like AI and ML were being used and, crucially, where they were not.
Encouragingly, when we spoke to senior IT decision makers in the sector, we found that most financial services firms are using ML in areas like compliance and to improve customer experience. We did uncover, however, that there are key areas that the sector is failing to tap into with ML and AI – predominantly in how it assesses its customer data.
Opportunities with data
Banks gather huge amounts of customer data. Most customers don’t switch bank accounts often, and as a result, organisations can collect years’ worth of information. To add to that, customers have constant and frequent contact with their bank, whether that’s online, via an app or in a branch. All of this presents a huge opportunity for the sector to learn about their customers in more depth but currently, that opportunity isn’t being realised.
When we spoke to IT decision makers in banking, we found that analysis of customer data is inconsistent. For example, the sector analyses its structured data, which includes things like names, addresses and credit card numbers. This allows banks to identify and block fraudulent activity in real time, as well a number of other benefits.
However, when we discussed how banks were using their unstructured data, which includes data that is stored as audio, video and email files and accounts for around 80% of the data banks hold, we found that very few firms utilised the information they were gathering. In fact, only three per cent of the organisations we spoke to are evaluating their unstructured data.
The missed opportunities here are huge. Analysing unstructured data with ML could help banks uncover important patterns in customer liaison and react to potential problems proactively. For example, it can analyse conversations and indicate that a customer may be on the brink of switching their current account or identify when a customer may be about to default on a debt payment. The technology would highlight these in real-time, allowing the banks to take the precautionary steps to prevent the action from happening.
Overcoming legacy barriers
The biggest barrier for the banking sector is it’s large-scale and outdated IT infrastructure, with 92% of the world’s top 100 banks still relying on legacy systems.
Indeed, all industries that have undergone a digital transformation have needed to invest a lot of time and capital into updating old IT systems. But for the banks, the scale of their IT infrastructure is enormous and switching from on-premise systems to cloud-based requires huge levels of planning and investment. But doing so unlocks a whole treasure trove of opportunities for the industry, particularly as traditional banks battle with challenger banks. The digital transformation could truly amplify the financial sector’s returns – with far greater impact than anything we’ve seen before in the industry.
No sector or organisation has been able to undergo a digital transformation overnight, and while it does require time and investment in the short, the long-term benefits are huge. By failing to embrace technologies like ML and AI, traditional banks risk falling further behind other sectors on their road to digital.