Natural language search: Helping banks enhance customer experience

Intelligent solutions enable self-service for both customers and support agents.

By Ryan Welsh

Ask anyone involved in supporting or servicing a customer in the financial services sector and they will certainly say that habits are changing and expectations are high.

This shift started with digital devices and the multiple customer engagement channels those devices have enabled. Organizations are required to provide timely, meaningful customer communications and responses to increasingly complicated customer questions. Technology will continue to change how financial organizations operate and engage with increasingly digital-savvy customers, so understanding the various emerging technology solutions is imperative to ensuring loyalty and improving customer satisfaction, while achieving operational efficiency in the post-pandemic era.

Today’s consumers, especially millennials and Gen Z, want to be self-sufficient. Not only do they prefer discovering information on their own through digital channels, but they seek to resolve their issues themselves without having to call customer support. In fact, 89 percent of customers say they expect access to a self-service portal when dealing with everyday problems.

Technical hurdles to achieving customer support excellence

To address these changing customer needs and improve efficiency, many financial service institutions have turned to emerging technologies including chatbots, virtual assistants and other artificial intelligence-based solutions to provide individuals with a do-it-yourself way to problem-solve. The issue is that these solutions cannot understand most questions and respond with relevant answers, at least not yet. A recent McKinsey study found that while more than 70 percent of organizations surveyed have deployed AI chatbots and IVRs, only 10 percent said they have seen real customer adoption.

What about the traditional search that has been around for decades? The majority of existing search solutions are keyword-based and can therefore only handle simple word-matching. They do not understand human language in either the question or the underlying text that contains the answer.

When it comes to addressing product and service-related questions, the system has to first be able to correctly interpret the specific question raised by the customer. Once done, it then needs to return the relevant information living somewhere in the support documents—which is often text-based data—back to the customer based on this clear understanding of the question. To do this well requires a different search approach than traditional keyword-based search.

When customers are not able to use the available tools to find the right answers, they have no choice but to escalate the case to a support agent. If the customer support person also cannot quickly find the answers, the customer experience suffers and operational cost rises, resulting in negative business outcomes including customer churn, lost revenue and damaged brand reputation.

Breaking down barriers to effective customer service

To empower customer self-service and improve support quality, financial firms are looking into different enabling tools such as natural language-powered search to help users find correct answers quickly. These intelligent solutions enable self-service for both customers and support agents, allowing them to ask questions using their own words as if they were speaking to a person, and return highly relevant, contextual answers instantaneously. The customer or support person merely needs to type a question into a search bar or a chatbot interface and they can receive the right answers in seconds. For financial services firms, this means faster time to issue resolution, greater customer satisfaction, and reduction in support tickets and case escalations.

Natural language processing technology has improved greatly in recent years. It now understands domain-specific, complex questions intuitively–even when they contain confusing phrases and complex intent.

It can also detect the same question phrased in different ways and bring the right answers back to support teams and/or customers alike, giving users the freedom to speak in their preferred way instead of having to learn predefined keywords and syntax. For example, questions such as, “How can I transfer money between my accounts?” and “How do I move my money from one account to another?” should yield similar answers. When customers and support employees can easily find the answers they are looking for with an intelligent solution, they will become accustomed to using it every time they have questions without getting frustrated or burned out on futile search. This benefit can be enabled without months of expensive training and work by AI teams.

How natural language processing is yielding big returns

Few will argue that customer service and support is one of the most important factors in deciding whether a customer will remain loyal to your institution. Providing great customer service is critical for customer retention, yet many struggle to accomplish this. Leveraging advanced solutions powered by artificial intelligence and machine learning can help financial organizations understand their customers better and address their questions effectively, resulting in a significant reduction in support tickets and call escalation—which ultimately leads to positive customer experience and improved business outcomes.

Ryan Welsh is the founder and CEO of Kyndi, a global provider of the Kyndi Platform for the Natural-Language-Enabled Enterprise, an AI-powered platform.To learn more visit or follow on LinkedIn and Twitter.