How Data Improves Digital Experiences for Financial Services

Historically, financial services consumers have been limited to one-way digital experiences: checking a website, receiving an email, or managing their account online. But today, conversational AI is enabling search engines and chatbots to "talk back" to consumers. They'll provide relevant answers because they can understand the question.

This is monumental: the consumer experience will change radically and rapidly, thanks to this technology.

Even so, there are obstacles to overcome. Financial institutions manage multiple digital channels and experiences. In addition to their website, this includes third-party listings (for branches, professionals, ATMs, and more), site search, support search, workplace solutions, and much more.

More channels means more data to manage, and across more systems. And even one piece of out-of-date information in this ecosystem can create an unpleasant digital experience for the consumer.

Thus, financial institutions need to use fewer systems and encourage seamless interaction between the ones that remain. Additionally, these systems must be easy to build, support, and maintain. This is how – and why – financial institutions are leveraging new technology to optimize their customers' digital experiences across a variety of channels and journeys.

Financial institutions are in a unique position. Unlike other industries, banks and other financial service providers have historically been subject to strict regulations, such as the Investment Advisor Act. These regulations (and the fear of crossing them unintentionally) have stalled the industry's embrace of the modern financial consumer's omnichannel journey. However, as financial institutions move away from legacy systems and make a concerted effort to update their operations, they're also emboldened to modernize their customer experience.

Today's customer expects easy access to information across every touchpoint, including listings, website, social media, and even third-party review sites. But enterprise financial institutions and service providers store tons of data on advisors, products, services, contact information, self-service help, and more. Unfortunately, the data can get disconnected when it lives in various systems – which it often does. This results in data silos.

Data silos are dangerous for several reasons. They make it harder to gain customer insights because users only have access to small pieces of the puzzle, making it difficult to see the full picture. Even if users are able to work past data governance issues and can piecemeal these insights together in custom dashboards that pull data from various systems and sources, it requires weeks – if not months – of coordination between departments, system administrators, analysts, and any other stakeholders to develop.

But data silos aren't just an internal headache. They can actually cause the customer journey to fracture. When someone can't find the information they need – or worse, they find conflicting information – then the entire customer experience is in jeopardy. If the financial institution doesn't have some kind of functional search experience, such as a natural language help search engine or a conversational chatbot, the customer may turn to non-owned resources for information about the business.

Finally, keeping the data up to date isn't the only concern. Typical Static Site Generators (SSG's) re-build the entire website every time there is a change in underlying data. For large sites with constant updates, this can cause lags and site crashes.

For the enterprise financial institution, this simply isn't a scalable response to data management.

Financial institutions are already using data to create a better customer experience and even capture new markets. Leveraging new technologies brings old-fashioned customer service into the digital age. However, they're being strategic with their investments.

1. They've Replaced Their Multiple Data Systems With A Knowledge Graph

Businesses can't take advantage of new technology – let alone AI – when facing data silos. As a result, companies are turning to a knowledge graph.

A knowledge graph uses entity relationships to optimize an organization's content for third-party search, voice search, chatbots, and other sophisticated AI. A knowledge graph is designed to help enterprise organizations maximize their techstack's various capabilities by providing current, up-to-date information.

This is an active approach to dismantling data silos. Additionally, the customer experience improves because the risk of encountering out-of-date or incorrect information is lowered significantly.

2. They're Managing Experiences With Bidirectional, Conversational AI

Speaking of the customer experience, companies are beginning to leverage conversational AI as a tool to surface resources and information for curious consumers. There are several business use cases for conversational AI, including search and chat. However, conversational AI is only useful when the data is organized, which is why it's most powerful when integrated with a knowledge graph.

AI-powered site search serves customers at every stage of the funnel. When used for support, it's a powerful tool for mitigating customer issues. Site search should surface resources such as FAQs, location-specific information, product-specific information, and more.

Some banks report a 50% to 70% reduction in unnecessary support call volume, which they attribute to improved search functionality. Natural language search – attached to a knowledge graph, for easy management of data – is a powerful tool for managing the customer experience by immediately supplying requested information.

Chatbots powered with conversational AI also serve customers throughout their journey. This is different from guided flows, which populate the question as well as 2-3 answers to choose from. With AI-powered chat, the chatbot will respond to the customer's question with the information needed.

Traditionally, chatbots direct the customer to another resource to find the information for themselves. In this new model, customers receive answers to the questions without ever having to leave the webpage.

3. They're Focusing on Presentation and User Experience

Finally, financial institutions are zeroing in on presentation. When all of the information is right there on the webpage, but customers still can't find it, it's usually chalked up to issues with the user interface (UI). But sometimes, it indicates a bigger problem: missing or incorrect data.

Enterprise organizations in particular can fall victim. With multiple locations to manage, even branch-specific contact information can be difficult to keep current. This is another reason why data management in a knowledge graph is absolutely essential.

When data is managed in a knowledge graph, any updates can actually be streamed directly onto the webpage via static generation. This eliminates lags, delays, and site crashes stemming from multiple updates.

The result is an improved digital customer experience, where the information is always correct and always available.

Financial institutions may have been slow to adopt modern technology, but now, there's no going back. As consumers have great digital experiences across industries and websites, the brands that didn't live up to their expectations will stand out.

At their core, great digital experiences are dependent on great data management. And while financial institutions used to struggle to find and invest in the best data management tools, today they can leverage content management systems (like a knowledge graph) to keep everything organized, up-to-date, and easily accessible.

Once there's a system of data management in place, financial institutions can leverage next-generation tools like conversational AI, natural language search, and more. The result is constant, scalable improvement in the customer's digital experience.