The financial services industry holds an unprecedented volume of information about its customers. Banks and insurers are floating on oceans of such data, of every conceivable type and in a multitude of formats. Institutions are having to handle an increase in volume and variety not only of internal data but of external data as well. This might be coming from market research bodies, aggregation sites or Fintech marketplaces. In addition, the customer base they are addressing now needs to be segmented to a much more granular degree than it was before to allow for the analytics required for personalisation of actions, advice and new products. This adds even more pressure in terms of the volume of data coming at them and the speed at which it must be processed and analysed. 

This data should, of course, be seen not only as a challenge but as a valuable resource. But as any CIO will tell you, data is of minimal use unless it is available in the right place at the right time. Half a minute after the customer has hung up the phone is way too late to tell them about your exciting new loan rates. The moment has passed, and the timing of any future dialogue is uncertain.

In an idealised scenario, every piece of relevant information would be available at every point of contact – whether human or virtual. Every touchpoint would be rich with opportunities to engage, satisfy and delight. Not only would the customer’s initial point of concern be swiftly dealt with, but a range of other possibilities could be brought into play as well. The customer journey would be immeasurably enriched, and no stone left unturned to provide them not only with what they wanted but with what they didn’t even know about. 

Let’s be clear that this sort of 360 degree analytical potential is not simply a matter of deploying the latest Customer Relationship Management (CRM) software suite. The level of immediacy we are talking about goes much deeper than that, requiring a complete rethink of the very architecture of a bank’s IT. Through the deployment of so-called Event Driven Architecture (EDA) a bank or insurer is able to deploy every meaningful bit of information on a customer when it matters. To deconstruct the jargon for a second, an ‘event’ translates roughly as a ‘business moment’, in other words the precise context of that particular engagement. We could be talking about an engagement face to face in a branch, over the phone, or one handled online. In all cases, EDA allows the right bits of information to be distributed to the appropriate places to act as the basis of informed decisions and effective actions. 

If a customer has more than one issue on their agenda, then EDA means you don’t have to make them wait while each is handled in turn. All can be serviced at the same time. Relationships between separate events can be identified by AI and acted on simultaneously. Let’s say a customer applies to refinance a mortgage, but at the same time wants to explore ways of paying off a credit card. These two separate events could combine to create a whole new event, let’s say by automatically offering the customer a personal loan through their mobile device at a special rate. The right architecture will be flexible enough to suggest possibilities like this quickly and seamlessly. There is a wealth of ways that a bank can understand and take action on the large volume of events that occur within its ecosystem.

Timing is everything. Analytics tools already exist that can prompt an action, but the hard bit is making it all happen in a timescale that makes that action relevant and contextual. EDA is not about interrogating some distant silo of information and waiting for an answer. It’s about taking action in the here and now. Speedy reactivity is at the heart of it and we’re talking about analytics not just in real-time but at enterprise scale. Think of EDA as an automated way to deliver the responsive and tailored experience your customers expect, and doing this in a secure, reliable and scalable manner.

EDA is already being deployed by a variety of institutions. Let’s consider a couple of real-life examples from our customers. First, there’s the case of a major UK-based insurer that wanted a way to improve the customer experience with omni-channel cross product engagements. It was challenged with not being able to provide a unified experience to customers spanning different types of cover.

The insurer was also keenly aware of the need to be more agile in a crowded marketplace, able to deploy products to market more rapidly as well as respond better to any market disruption. With EDA it is now able to abstract and correlate meaningful business information from different events. Data is processed in the context of current as well as past events. Real-time event processing is providing instant awareness and timely decisions, helping to drive competitive differentiation, superior customer satisfaction and higher revenues.

The second instance involves a large Canadian bank which was unhappy about the inconsistent experience it was offering across different channels. Its existing architecture left it with limited visibility and an inability to react to changing customer behaviours, leaving it poorly positioned to provide relevant offers in real-time. With EDA it can now identify key incidents and automate processes, as well as visualise data better leading to better decision making and more satisfied customers. 

In both instances the institution has leveraged EDA to connect siloed systems, understand who the customer is and then react through the right channel with the right decision.

AI and robotics have already taken banks and insurers some of the way towards this goal, but without a shift in the general architecture that needs to go with these advances, benefits are being missed. EDA offers a whole new way to find correlations between bits of information, allowing you to take specific actions that would not otherwise have been possible. It’s not about ripping up everything and starting again. EDA is a new frontier, but one that plays well with investments that banks have already made in areas like data mesh, hyper-personalisation and connected intelligence.