This blog series highlights the benefits of implementing chatbots in financial service to exceed customer expectations. This post explores chatbot technology in the financial services industry.
Chatbots, often referred to as virtual assistants and interactive agents, are software applications that can interpret human speech and texts. They can execute transactions via an organization’s systems, providing appropriate responses back to the requestor.
To be effective, chatbots must interpret each exchange in a dialog in the context of the conversation. As such, the semantic processing engines of chatbots are combined with machine learning (ML) technologies in order to provide human-like intelligent responses throughout the exchange. Chatbots have to be trained, both initially and throughout their use, to understand the specific terminology and phrasing of their user community.
Businesses leveraging the two technologies together would now be able to harness their data for critical insights and predictions, connect customer touchpoints across their business, and drive brand loyalty and growth.
AI technology is evolving rapidly, and many chatbot software providers are incorporating the latest advances into their products. These leading edge chatbots provide multiple semantic processors to expand the range of their capability to understand language and add sentiment engines to be able to detect the emotional state of the user. These “emotionally aware” chatbots can determine and rank a user’s attitude along a variety of measures such as anger, disgust, fear, sadness, joy, and positivity, on both a per-exchange basis or across the entire dialog.
Some chatbots even have the ability to have a dialog interrupted by a sidebar user request without a loss of contextual continuity, in a similar manner to a human representative. For example, imagine that during a customer service dialog resolving an issue with a recent account fee, the client asks or types, “What was my balance as of last month?” The chatbot can temporarily pend the transaction inquiry, lookup the balance as requested, provide the information to the client, then return to servicing the original request. Although the technology has not yet achieved the mark, the goal is for chatbots to pass the “Turing test,” wherein a machine’s ability to exhibit intelligent behavior is equivalent to, or indistinguishable from, that of a human.
Due to AI and ML chatbot technology’s ability to disrupt the customer service contact center paradigm, analysts forecast the global chatbot market to grow at a CAGR of 24.43% during the period 2018-2022, and reach $1.25 billion by 2025.
To learn more about the trends and capabilities in AI chatbots, options for integrating them into client service workflows, and how they can reduce expenses and increase revenue in the process you can download our guide here, or click the link below.