How Artificial Intelligence Can Create an Attractive User Experience in Financial Services

How Artificial Intelligence Can Create an Attractive User Experience in Financial Services

The pace of digital innovation has raised the bar for customer experience, and there are two market trends in particular that financial services providers must consider to remain competitive against their digital-native counterparts in fintech. The first is that the number of customers who favour digital channels has increased dramatically in recent years, and the second is that customers expect services to accommodate individual circumstances and preferences in real-time – especially when it comes to personal finances.

While a strong digital transformation roadmap can help your company adapt and scale to meet the demand for more digital experiences, realising the level of personalisation customers have come to expect requires a more complex approach.

The most popular method for personalisation to date, segmentation, classifies users based on specific types of data. This is captured via demographics, purchase history and other details extracted from your CRM or interactions with your support team using text mining — including other clever sources that have been engineered into the user experience (UX). Your data science team can use this data to group users into clusters labelled with actionable names. These clusters are used to train a model for classifying new prospects according to the labels, and your machine learning operations guarantee that the model performs as expected when it goes into production. Equipped with these insights, your marketing team is ready to increase conversions, right? Not so fast.

While segmentation can offer valuable insights, this wealth of information is only as good as the overall customer experience. For example, what good is it to know which segment a customer belongs to if it takes too long to process their loan application or reply to their enquiry? That’s where automation introduces an opportunity for optimising your approach to personalisation. Automating business processes at scale via robotic process automation (RPA) can increase your firm’s productivity and accuracy. Since RPA is no longer limited to performing menial, repetitive tasks, adding it into the mix with AI modules will enable the automation of complex end-to-end tasks involving judgment, image recognition and text processing.

Think about how much time you can save your teams by automating tasks, enabling them to focus on more complex and creative projects. There’s also the added value you generate by preserving and scaling the know-how that is embedded in your business processes. Furthermore, you can use smart assistants to provide a better service by not only understanding the intent of your customers but also having access to the relevant knowledge bases.

So far, so good. You can understand your customers and provide low response times via automation. But is this enough? Are you providing a best-in-class customer experience? Well, that depends on whether you are constantly improving your products.

Building continuous testing techniques into your product development lifecycle is just as important as the initial product design and implementation. A/B testing is one such technique that businesses leverage to hypothesise about how to improve engagement and conversions. It is useful for testing hypotheses about user behaviour through experiments based on well-defined modifications to the UX. By applying this process repeatedly, you will find that certain changes produce the desired results.

But even with this scientific approach to improving UX, you may fall short in meeting more complex demands for tailored services.

Imagine for a second the potential value-add if your UX could adapt to the particular cognitive styles of users in real-time. This is feasible with the application of cognitive architectures – theoretical models that account for complex cognition – in AI. Cognitive architecture provides the modules (visual, motor, procedural, declarative, etc.) and mechanisms to communicate among the modules. This requires the knowledge necessary for the task and the means to see and interact with the graphical interfaces. You can also control the model’s behaviour with reinforcement learning, which rewards those actions that help to achieve your goals.

With a cognitive model in place, you can track the cognitive, perceptual and motor actions of users down to the millisecond. At this level of granularity, it’s easy to detect subtle differences in the psychomotor abilities of the end-users – your customers – and understand that some of them use memory-intensive strategies while others rely on efficient perceptual actions. By combining a cognitive model with your clickstream data, you can start adapting interfaces to accommodate the individual preferences of your customers in real-time to curate the ultimate customer experience – a clear gamechanger for financial services.

Wizeline helps financial companies leverage leading technologies to accelerate digital transformation and deliver better customer experiences. Visit our landing page or email to learn more.

This article was first published in the Business Reporter.

Aisha Owolabi

Posted by Aisha Owolabi on June 9, 2022