Really really really Know Your Clients: Fintechs hone behavior analytics and AI best-practices

The next generation of digitisation in financial services will propel the industry into the knowledge economy. More specifically, financial services firms can look forward to a future of using a more contextual knowledge and understanding of their clients to generate tangible economic value.

How much time do your customers typically spend on your website? And what are you doing to encourage them to engage more frequently, invest more time with your brand and use more of your services? Even if you’ve created a dynamic, customisable user-interface enjoyed by your customers to access a range of services easily and simply across multiple devices, you can still do more to service their needs in this next wave of ‘data driven’ digitisation in financial services.

Take a minute to consider a client’s total aggregate time. Perhaps, if you’re lucky, that client will spend a fraction of their total time on your digital channels. Thus the key for you is to make optimal use of the time that the client spends outside of your digital channels. As an example, a 2015 report by Ofcom, the UK communications regulator, found that the amount of time Britons spend online had doubled in the previous decade, largely thanks to increasing use of smart phones and tablets. This will rise further as the quality and range of available services continues to surge. Consequently, it is important for any business which has growth ambitions to ensure it is open to opportunities to find out more about customer behaviour via their clients online activity in order to better understand and meet their needs, thereby increasing customer loyalty and ensuring sustainable profitability.

Let’s take the consumer industry as an example. Online retail giants such as Amazon are already well-advanced in this respect, having pioneered algorithmic recommendation engines based on machine learning that is able to accurately endorse future purchases with a high potential of interest to its users, based on a combination of navigation behaviour and purchasing activity both on the individual and aggregated level. The difference now is that this capability is no longer confined to Amazon’s own site or apps. When an Amazon customer accepts cookies on its site, the firm is then able to collect real-time behavioural, contextual and time related data which when combined with its  existing information on the user’s Amazon site navigation and purchasing preferences, can make even more accurate predictions on the return of that customer to the Amazon site. Moreover, Amazon has the ability to place highly targeted content on other sites frequently visited by the user, for example promoting books or other products available on Amazon that fit the customer’s current interests.

How might this translate to digitised financial services? Self-evidently, by really really really getting to know your clients, you are better placed to serve them. If you know your client has a particular interest or need, you can act on this by ensuring it is reflected on your customisable user-interface (not just, ‘Hello customer’, but, ‘Welcome back customer, can I provide you with some information on..?’). More importantly you can also draw the customer back to your user interface by ensuring contextually relevant content (that you are probably already producing) is highlighted in your online marketing presence on other sites and targeted specifically to behavioural activities of the individual client. This is increasingly possible thanks to the next generation of fintechs bringing these consumer industry best-practices in behavioural analytics and machine learning to financial services firms. 

Similarly, a client might visit an investor platform to research some stocks for potential purchase, perhaps placing them on a personal watchlist, before going off to other sites to do further research. If the client takes the investment advisor’s cookie with him, the firm will be able to track the browsing data collected before the client returns to the platform to make the purchase. Following on, the firm can use the purchasing preference data alongside behavioural data and other information to add further context to known investment interests, thus building a more accurate picture of the potential investment needs of the client. In addition, the investment platform may be able to post appropriately targeted advice, or deliver contextual content relating to future stock purchases, either from its own resources, partner firms and/or other third parties. This can not only improve the tailored user experience but from a compliance and regulatory point-of-view this has the potential to drive a more suitable and responsible investment environment for the consumer if executed correctly of course.  

Many banks are beginning to embrace digital transformation, recognising the opportunities to enhance the user experience by delivering customisable services across multiple platforms and, where appropriate, sourcing complementary services from third parties via open APIs. But the full potential of such investments will not be realised if banks and other financial services firms don’t continue to push the boundaries, exploring the opportunities of digitisation to better understand and fulfil clients’ needs and desires. Big data analytics and artificial intelligence programmes that have been honed in consumer-facing sectors and are becoming increasingly available to banks, enabling them to match more accurately their marketing messages to customers’ fast-changing priorities, thereby increasing interaction levels and, ideally, transaction volumes. Converting client interests into financial transactions is the ultimate goal; generating tangible value from the knowledge economy. But not only that, these same programmes can also aid in the development of more engaging and accurate methods of evaluating a client’s knowledge, experience, appropriateness, and investment risk tolerances to better equip these firms at presenting the right investment opportunities to specific investors, thus aiding in the sustainability of the entire industry.   

In many cases, these tools can be accessed in the same fashion as the complementary services that banks are already using to redefine their value proposition in the digital age of APIs and open collaboration. The additional opportunities they offer for enhanced customer satisfaction and customer 'stickiness' make it even more imperative for banks to have a highly flexible and easily integrated technology infrastructure. In some respects, this amounts to building a digital feedback loop. By augmenting your information about clients’ needs and behaviours, you are better placed to attract customers to your digital channels on a more regular basis and to continually evolve the user-experience you offer across your various platforms – all facilitated by an underlying infrastructure that permits you to mix, match and revise the services you offer in response to data driven modelling of consumer demands.

In ‘The Field of Dreams’, Kevin Costner’s mantra was ‘If you build it, they will come’. In today’s digital economy, banks do not have to leave it to chance.