Using AI to Enhance UX in Wealth Management Code

AI has the potential to advance the user experience beyond just computer algorithms processing data to also assisting with rapid trades. Among the various possibilities is enabling a seamless, frictionless, and personalized multi-channel banking experience.

Over the next 20 years, nearly $30 trillion of wealth will transfer from Baby Boomers to younger generations, resulting in a new population of individuals requiring intuitive wealth management. These younger, tech-savvy generations expect more advanced digital capabilities from their wealth managers, as digital transactions and FinTech continue to grow in popularity. This trend demands a redesign in existing service models, and AI has the potential to advance the user experience (UX) beyond just computer algorithms processing data to also assisting with rapid trades. Among the various possibilities is enabling a seamless, frictionless, and personalized multi-channel banking experience.

Traditionally, A/B testing and smart assistants have been effective in continuously improving the customer experience. As younger generations demand more advanced offerings such as tailored and contextual services, 24x7 availability, real-time updates and access to opportunity, companies can consider a more sophisticated approach.

An enhanced UX demands the intersection of multiple disciplines, including engineering, graphical design and a third component, cognitive modeling. Cognitive models are detailed accounts of human cognition interacting with user interfaces. Essentially, a cognitive model acts like a human by replicating cognitive (reasoning/thinking) processes that humans experience to complete a task in milliseconds. These models run on top of cognitive architectures, which are theories, implemented in software, of how the mind works as a whole.

Let us use the simple task of logging into your site as an example. The mere task of logging in sets a goal for the model. A cognitive model works with two kinds of knowledge: declarative and procedural. Declarative knowledge relates to your user name and password, which is required in this case. Procedural knowledge relates to how to interact with a login interface, which is also required. To enhance the UX for logging in to your site, we need to connect a cognitive model to this interface and later extract the set of detailed actions required to interact with it.

Because we are using AI to replicate intelligent behavior, the first step is to perceive the elements of the user interface (i.e. moving your mouse to the login section and typing your user name into the textbox). To do this, a semantic segmentation process is run on the user interface, which detects the nature and location of visual elements. These elements are later sent to the cognitive model via a "bridge" developed with a programming language. The cognitive model receives these elements and starts triggering a set of procedural rules that represents the knowledge required to interact with the interface. From this point, a series of messages are transmitted to and from the model.

Consider the action of introducing your user name. You need approximately 50 milliseconds to retrieve your user name from your Long Term Memory (LTM), and 200 milliseconds to visually locate the appropriate textbox for input. It takes another 500 milliseconds to move the mouse and click on the textbox. Depending on the length of your user name, it would take around one to two seconds. The advantage of cognitive modeling is that all of this data is recorded, detecting opportunities to enhance the UX.

Now consider your model takes an extra 500 milliseconds between capturing your user name and capturing your password, and the manual actions are taking longer than expected. The next step is to formulate a hypothesis, such as making the password textbox more conspicuous to reduce the time needed to login. You can make the changes to the interface and run the model to confirm or reject your hypothesis, but you also need to test it with actual people.

Cognitive modeling is a fine-grained approach and a sure way to achieving an enhanced UX for this new segment of young investors. Applying this approach to key customer journeys may result in a significant gain for the organization.