Many analysts recommend that financial institutions start the digital transformation process with small incremental solutions. The problem with that view is that it ignores the broader data, analytics, and solution perspective required for a more challenging future.
Because it is simpler, many financial institutions focus on small details and fail to look at the bigger possibilities. They identify a small process, and “bring in the bots” with robotic process automation (RPA) applied on top of traditional operations IT systems, such as content management systems (CMS) and business process management (BPM) platforms.
On paper, the benefits seem obvious, bots work 24/7, don’t require vacations and won’t demand pension contributions. In the use cases and implementations seen to date, margins for operational processes (that are viewed as a cost center) increase, as does accuracy.
So, why do you think there’s a “but’ coming…….?
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Solutions Tethered to The Past
RPA, in particular, is a gateway to introducing enhanced cognitive capabilities to financial institutions. However, on day one, it will not magically resolve data quality issues or existing platform limitations. If a process is fundamentally flawed or broken, and this is converted from human-owned to robot-owned, the underlying data quality or connectivity issues that existed in the old world will also exist in the new.
These are point solutions. Worse, the technology tethers you to the past, because the automated process has broadly replicated the human process. Money has been spent. Egos are on the line. Further change will be resisted. And, all the while, the CEO has a stream of great press releases about how the company is “leveraging AI” as part of its transformation journey.
Establishing a culture of imagination to deliver a vision of the future is one of the biggest challenges banks face today. Risk-averse practices, compliance requirements, and sensitivity around data are often cited as the barriers to why it doesn’t occur. But those are merely handy excuses for maintaining the status quo — a mindset for using bots to refine flawed processes rather than examine if the process even needs to exist in the first place.
When you have accepted that you have to hollow out all of your legacy systems and siphon the data into the cloud – where you can find it and actually do something with it — then you will begin to create a truly enhanced customer experience and value exchange.
5 Steps to Build a Stronger Data Process
A valuable approach for processing data is : Find, Augment, Analyze, Listen and Act. So, if the data is in the cloud – well done, because you can Find it. Next step is to Augment the internal data so that you can build different perspectives on each consumer.
Assembling richer pools of data from multiple sources will give banks and credit unions more angles from which to view consumers and more ways to understand them than can be gained from transactional information alone. Financial institutions will be able to profile and segment consumers more precisely and look for behavioral patterns that can be used to refine predictive models. The insights gained will power new services and improve existing ones.
Analyzing data sets and identifying unmet and under-served consumer needs highlights the third step. Remember to balance the ubiquitous data science with data artistry – valuing psychologists, semioticians, ethnographers, and ethicists to temper raw output with actual human insight.
Imagine these needs popping up in the intersections of a Venn diagram; for example, if you bring utility data together with banking transaction understanding, you can create new insights into that data and the potential for new services built upon that understanding.
Next, you must Listen to what the insight tells you, and use it to develop new hyper-personalized, predictive and preemptive services for your customers. Finally, and most importantly, you must Act on the insights to make the customer experience change.
Big Tech Leading the Way
To see how the use of data and advanced analytics can impact the consumer experience, you have to look no further than any of the larger tech firms. Firms like Amazon, Facebook, Google, Apple, Alibaba, Tencent and others already are leveraging data insights to reduce costs, improve processes, eliminate friction and improve experiences.
Some more enlightened banks are also on this journey. They are taking the historic data set of customers who have fallen into financial distress and, using machine learning, identifying the signature patterns in their spending behavior and demographics.
That allows the banks to build a predictive model to flag signs of approaching problems and when to intervene to help them preempt the situation. Consumers could then receive support when it would benefit them the most, and banks could reduce the time and money they spend helping people after they get into trouble.
Such insights by themselves are great. But unless financial institutions do something with these insights there is no real value. Banks and credit unions must dig into the less obvious spaces, building competitive advantages, with AI having a major role in this work.
One of the challenges is that there is a dangerous failure of creativity, borne out in the industry’s response to change by innovating in small pockets. More than simple technology expertise, bank leadership now needs the imagination to see a world of hyper-personalized, predictive and preemptive services, powered by cognitive technologies.
Deploying AI at scale in this way needs boards and executive teams with imagination, creativity, and courage. Imagination to soar above the temptation of restrictive incrementalism, creativity to work out how to take advantage of exponential change, courage to take a leading role in the open banking future. The result cannot be an apologetic ‘fast follower’ solution, with a smiley chatbot chuntering around the company website, pestering visitors.