What Amazon, Spotify and the next Generation of Financial Sales have in Common
Artificial intelligence is beginning to permeate all of our lives in a myriad of ways. Potential use cases seem endless. But, as with all great opportunities, AI brings risks, too.
Aware of these risks, the heavily regulated financial services industry has been slow to adopt AI-led technologies. But it’s happening.
Given the hyperbole — much of it inspired by science fiction rather than present day reality — it’s important to understand what AI can do to help us better serve clients. In order to identify these opportunities, we must begin by breaking down what AI is and what it is not.
AI that is being integrated into financial services is “weak AI”, or “narrow AI”, since it performs narrow, clearly defined tasks that are normally reliant upon human effort and intelligence. For the most part, the machine can perform the task alone for an (almost) unlimited period of time. However, in the first instance, a machine still needs to be shown what to do and a human still oversees their activity.
Narrow AI is having a huge impact on many areas of our lives. Financial services offers machines it’s favourite fuel in the form of data, so, once we’ve worked out high impact use cases for narrow AI, deployment in finance should be quick, not to mention transformative.
The client is everything
When discerning how narrow AI can help in financial services, we should focus not upon the machine, but upon the human that the machine is designed to help: the client.
We know that clients have a number of problems they’re trying to solve. For example, we know clients want great advisors on hand at convenient times, pitching opportunities in a timely manner. They demand the best levels of service for the lowest cost. They know their business is valuable, and so they’re prepared to take it elsewhere if they don’t get what they want.
Brokerages, wealth management firms and banks are finding it increasingly hard to service clients. Clients are more informed than ever, which means salespeople have to be at the top of their game, especially given client volumes and thinning commissions. The cost equation of servicing all clients doesn’t add up. Pragmatic salespeople instead are beholden to the 80:20 principle, choosing to focus on high value clients and forgetting the rest, who grow frustrated and leave.
This churn has grown because we have strayed as an industry from focusing on what is right for the client. That user-centric approach is not possible in an age where everyone demands constant attention, speed of communication and execution. As a result, clients lose patience.
Similar trends can be identified in other industries. Their story is our story, too. In retail, shoppers grew frustrated by queuing for checkouts, so they visited Amazon, where they no longer have to wait. In entertainment, fans didn’t want to wait six months for a film or a week for the next episode of their favourite series, so they now sit on the sofa and click to the next one, streamed to the device of their choosing. The same can be said of Uber, Airbnb, Spotify and others.
User-centric and informed from mining petabytes of data, the likes of Amazon and Spotify are hugely successful as evidenced by rapid growth in paid subscription-generated revenue. The key take out here is that users and clients expect and will pay handsomely for just-in-time personalised service, delivered at scale, 24/7.
User-centricity as an enabler
When we focus on the core of what AI can do for our clients, we begin to see the possibilities. Shopping, music, television and transportation show us the way. Using machine learning, these platforms focus the user experience on one thing only: what the client wants.
These methods and technologies can be applied in finance, too. Customer expectations are now being driven by seamless, digital experiences that improve the customer’s user experience. In finance, we must seek to introduce the same practices by augmenting salespeople with machine learning methodologies that allow them to service clients in a more user-centric way.
As these systems improve, like the Amazon and Netflix recommender algorithms that improve over time as they learn the nuances and tastes of both you and your peers, salespeople will be able to service a larger number of clients in less time, at a lower cost. In short, as machine learning effectively integrates with financial services, user-centricity will return, too.
There will be challenges, as increasing amounts of data needs to be stored and processed in a compliant, secure and immediately retrievable manner. There are also barriers to entry, as building this sort of complex approach, with machine learning being applied to data sets of extreme volume, velocity and variability is extremely challenging.
However, as the likes of Jeff Bezos and Daniel Ek have shown, challenges offer huge opportunities for first movers. As they say, if it was easy, everybody would be doing it. So financial services companies bold enough to apply AI-led, user-centric approaches to the next generation of sales technologies will develop deeper, stronger and longer-lasting relationships with more satisfied clients. As a consequence, by differentiating themselves in an environment of fee compression, they will be rewarded with revenue and growth.
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This article was created in association with Arkera, a revolutionary, AI-led app that connects real-world events to unique investment stories for your clients, giving them the confidence to invest more.