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Once a mainstay of the banking industry, current accounts are increasingly perceived as a utility product. Despite their maturity, they remain the second most complained about banking products in the UK, right after PPI. In recent years, costs have increased for everything from overdrafts to transaction fees, with average customer accounts unwittingly racking up £152 per year in hidden bank fees each year. At the same time, as banks proactively de-risk, customers have been making unfavourable headlines with their stories of having accounts closed with no explanation.
Compounding these issues, switch rates remain low – about 3% annually – and not for the right reasons. It’s notoriously difficult and time-consuming to switch in the UK, and customers don’t foresee any financial benefits after making the effort. In fact, over the past few years, only Nationwide has made good headway on this front.
Regulators are pushing for change – or certainly attempting
Regulators have clamped down with challenges on fees, minimum service level requirements, and mandates for cost transparency and reporting.This gives customers a degree oftransparency into their current account service, tools to objectively compare offerings across banks, and the ability to switch banks quickly and easily. And with the passing of open banking legislation, customers can now choose to have their account data shared with other banks and third parties. Regulators are of course hoping, open banking will drive competition, which will necessitate real innovation that benefits all parties.
But barriers to digitisation continue to stall creative efforts, chief among them being poor collaboration between business and IT and the lack of integration between new and existing technology and data. As a result, we’ve only witnessed baby steps in the name of innovation – for example:
However, given the pace of technological change today, these baby steps will quickly become table stakes and much of it needs to be proven with numbers in the P&L.
Fintechs such as Coconut, offering niche current accounts optimised for the self-employed and freelancers – a rapidly growing market segment in the UK – are innovating data driven, cloud-based account services that could dramatically impact people’s lives and solve real business problems. At the same time, new banking propositions are already harnessing the power of big data to transform traditional banking services with digital solutions. Examples include Monese’s banking option that lets people bank without a local address, Starling, Fidor and Monzo to name a few.
So what does all this mean for established banks?
Clearly it’s time to shake up and harness the extraordinary current account data to understand their customers and innovate to stay ahead of nimble, digital competitors. Machine learning (ML) is the key to making this possible.
Current accounts are the gateways to deeper customer insights
Machine learning is creating new possibilities for banks in terms of what they can do with their terabytes of customer data. They should already be mining their current account data for deep customer insights and delivering those insights in a personalised way at the right time, and through the right channel, to bring new value to customers.
At the core of their relationship with banks, customers leave behind a stamp of their activities (transactional and channel interactions), behaviours, and preferences. The result is massive amounts of data ready to be mined.
But are customers getting proportionate value back from the data that they willingly give to their bank? Isn’t it time for banks to deploy advanced machine learning algorithms to predict patterns (supervised ML) or give new, unknown insights (unsupervised ML) to make the relationship real-time, pre-emptive and proactive?
Within the current account product line, examples of ML use cases could include:
But ML ruccess requires a right implementation approach and mindset
As noted in my earlier blog , success with AI and machine learning-driven innovation is not a given. The use case and business justification must be robust and have strategic business relevance. There must be business readiness to implement and adopt ML technologies and preparation to deal with the operational and operating model impacts.
The ML opportunities are huge, and starting with current accounts, as the primary relationship product could deliver clear monetary benefits. Innovative technology vendors and change organisations are on stand-by to partner with banks, customers are on stand-by hoping banks will grab the opportunity, and regulators have called out the need for change. How long can your bank wait?
ML could be the key to unlocking the gateway to insight and innovation. Isn’t it time to call the “digital locksmiths”?