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The 3 i'sof Digital Banking

The first i is INNOVATION and proven by the hordes of Fintech’s disrupting almost every aspect of financial services through their use of technology and by challenging traditional business models. Yet it is still fascinating that many banks are still unable to grasp the difference between digitising existing business models versus the real threat from digital-only banks. Quoting, David Brear of 11FS, “we are not digitising banks, we are building digital banks”. I’m guessing what he means is that a true digital bank is more than a digital branded bank with omni-channel support, straight through processing and a move away from paper, people and physical branches. That, a digital bank will re-invent the traditional relationship with customers and or the products and services that banks offer today.

The second “i” is INTERACTION. As customers move online, managing customer interaction becomes a significant differentiator. The challenge is both technical and business. From a technology point of view it’s simply being able to track and trace customers through the increasing range of digital channels customers are adopting and in the future the sheer volume of data collected by doing so. The business challenge is however much more difficult, that comes from getting the customer’s permission to track and trace, then to carefully manage communication so customers don’t feel the bank is stalking them.

The third “i” is one that is talked about when discussing AI and Chatbots. However I do feel that it is one that is undervalued currently and that banks should start to give much more serious consideration to. The third “i” is INTENT. When it comes to chatbots the key task they undertake is to try and understand the customers INTENT. Exactly what are they are asking for and without asking try to understand WHY. The first part is simply undertaken by Natural Language Processing (NLP). Speech/text recognition simply recognises the words typed or said, NLP tries to translate that to an enquiry or action. For example NLP works out through machine learning that Pay, Transfer, Send all mean the same thing. Some bots are smarter, for example Kore create global and user dictionaries, the user dictionaries record words a specific person uses normally. And by “reflecting” the same language they can build a nice affinity with the person.

The next part is taking the customer request and using as wide a range of data as possible and combined with appropriate analytics try to understand the customer’s INTENT. For example sending £20 to friend, is that because it was his birthday? Had they previously been on a meal together and he was splitting the bill? Clearly having access to the customers Facebook account would help with answering both those questions if you have permission to do so. The translation of INTENTS is important as it is key not only to recognising sales opportunities, but to provide useful advice or create loyalty through timely rewards. So INTENT is a key part of INTERACTION management too.

Another aspect to INTENT is understanding timescales, the customers immediate INTENT versus an action now for a longer term INTENT, like wanting to send their kids to University, or saving for a car. This is not a real-time translation but one that is understood through historic data and patterns from customers of similar profiles.

Whilst the latter two “i” seems intrusive, executed in the right way they can be used to provide an invaluable service. The skill/accuracy when a bank understands customer intent, could be as much of a differentiator and competitive weapon as the products and services it sells!


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