For banks there is an existential challenge that must be confronted in 2017. Indeed it has been on agenda since 2012 and should have been faced at some point over the past five years.
Do something with the data
Banks like to see themselves as customer destinations physically, while consumers view them as simply components in a wider value chain. Nonetheless, banks still can’t shed this misconception and see themselves as important institutions and central to peoples’ lives.
They need to break with tradition finally and pursue entirely new models. These focus on outcomes rather than process or tradition, and adding value to how they help customers transact.
To spell it out most clearly, let banks become predictive forces in their customers’ lives. They have an incredible wealth of data on customers – spending habits, credit, lifestyle choices – that they could analyse and apply productively for their customers. Banks can take this to offer customers truly personalised, attractive services with offers and co-branding initiatives that are demonstrably valuable, rather than incidental to customers.
To date, banks have ‘sort of’ recognised this opportunity, but have been slow to do anything. The gamechangers they cannot ignore are the latest artificial intelligence (AI) technologies. These can be properly melded with a customer relationship and engagement management system to give banks an always-on predictive analytics to see, select and act on insights quickly and accurately. Combined with robotic process automation, banks can be responsive at speeds that customers desire and create the space to re-invent and personalise services.
Get close up to the consumer, warts ‘n all
Banks constantly look over their shoulders at other banks. Keeping an eye on the competition makes sense, but the lesson for the banks in 2017 is to be even more fixated on what customers really desire.
Taking a customer-first philosophy is about being realistic about customers’ motivations. Banks pursue the idea of loyalty and trust, but these values are not as important to today’s consumers. Customers should be treated as serial transactors that transact as it fits their level of convenience, with no obligation towards the bank.
This is a tough reality check for banks, but acknowledging it clears the way to move forward, rather than remain in the conflicted situation in which the industry finds itself today, i.e. made up of long-established institutions offering stability and services to everybody, that also are pressed to be more innovative, fast moving and customer-friendly (a situation exacerbated by successive governments’ oscillation between seeing banks as lepers, the cutting edge of the services economy, and instruments of social change).
Breaking the mold of banking hinges on how the banks that analyse and act upon more customer data in real time and context can manage risks more creatively and accurately. This means rethinking and creating new kinds of services, such as:
- ‘Lifetime value’ calculations would need recalibrating with a longer maturity period and expectations of higher promiscuity or customer disloyalty. This is likely to apply as much to the much-vaunted millennials as to the baby boomers who are entering their sixth decade of work or becoming the new retirees.
- The nature of property lending to younger people is going to need to change to reflect their graduate debt; for example with products that comprise a partial shared equity deal rather than purely deposit + mortgage and even ‘mortgage swaps’ between a part-owner who wants to move out and their ‘replacement’. The equity might well come from crowdfunding, perhaps including monies from people building for their own purchase.
- Creating services that are sensitive to an aging customer base will become more important. Significant financial decisions are being made in later life, such as what to do with pension pots or tax-vulnerable wealth. This requires banks to offer complex advisory services to a growing market and explore ways of moving funds through generations or ‘good causes’ without losing control/benefit during the redistributor’s lifetime. AI and machine learning can help staff to guide customers through these significant decisions.
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