Conversational AI in banking – three common mistakes companies make

​The financial services industry is often considered rigid and inflexible compared to other industries, and the legacy equipment and systems they use remain critical to certain financial processes. However, when it comes to going digital, the financial services industry is actually leading the way in implementing digital transformation initiatives. Research shows that the financial services industry is one of the most digitally mature industries, with a 28% success rate in digital transformation initiatives compared to other industries.

Emerging competition and changing work models have boosted the adoption of modern technologies in the ecosystem of the financial services industry. However, adoption and successful implementation are two different things, and several mistakes are being made that limit the benefits these financial services businesses can reap from the new technology. Conversational AI in customer experience is a technology that many large banks have adopted but have yet to reach their full potential.

Many banks have created proprietary chatbots to handle simple customer inquiries, or have partnered with vendors to install chatbots on their websites. While these can better serve customers, they also have limitations, mainly because not every chatbot is the same. Chatbots vary widely in their ability to hold conversations and process information and ultimately provide appropriate solutions to customers.

Research into customer experience using chatbots found that customers always have some complaints - 37% of respondents believe that communicating with chatbots is often demotivating and its limited capabilities Preprogrammed answers mean they can’t get to the bottom of the problem. In fact, most modern systems offer very little cognitive intelligence, enable little automation, and are limited in their ability to handle customer issues, often only providing ready-made answers to frequently asked questions.

So, how do financial services businesses get it right when it comes to implementing conversational AI? First, here are the three most common mistakes banks need to avoid when deploying these systems.

(1) Not Putting Customers First

Every bank wants to do this by automating the basic process of customer-employee interaction. Save money, but when considering implementing conversational AI, if the end goal doesn't help customers achieve their goals faster than traditional customer support methods, it should be reexamined before the project begins.

Customers don’t know or they don’t care about the limitations of the chatbots used by banks. If their initial question is answered, they may ask more complex follow-up questions or ask if a transaction is possible. A basic chatbot will answer these follow-up questions the only way it knows how, by submitting them to a bank employee for answer. The end result is a poor user experience that still requires human intervention (and avoiding human intervention is the primary goal of using bots), and customers may turn to time-consuming manual methods in the future instead of relying on ineffective bots. Essentially, if a business has invested in a program, that program may provide customers with an experience they don’t want or need.

(2) Not choosing the right tool for the job

If a bank decides to undergo digital transformation, it should look into more advanced conversational artificial intelligence Intelligent solutions to provide a higher level of investment protection and effectiveness rather than deploying a functionally simple chatbot that will quickly become outdated. Banks’ investments need to be as future-proof as possible, with conversational AI agents skilled enough to perform tasks based on expert and data-based decisions, and then learn and predict new scenarios from these interactions over time to satisfy customers at all times needs. First, banks should identify a number of processes and apply them to common business problems. In other words, they should respond to common or recurring questions asked by customers, and advanced AI solutions can deliver results without human interaction. Conversational AI systems are most valuable, especially in the short term, when they can help improve customer query response rates, handle times and first-contact resolution, as well as find the right workers to complete processes that cannot be automated.

For example, if a customer asks a question like “Should I apply for a small business loan?” a chatbot cannot provide a universal answer. With cognitive systems, banks can leverage machine learning, conversational differentiation and historical memory to provide informed opinions on customer questions and concerns. The cognitive system can study a consumer's banking history, access market data, perform calculations and, most importantly, query their financial goals in order to provide meaningful recommendations.

(3) Deployment too fast

Practice makes perfect in many things, even for digital workers. Enterprises need to be wary of promises from some vendors that develop AI systems that can be integrated into existing IT ecosystems and ready for customers within hours. Installing a conversational AI banking solution and training it to achieve the end goal are distinct scenarios, albeit related ones.

As conversational AI solutions continue to advance, banks can find solutions that follow strict processes, have a professional understanding of banking terminology, and provide seamless integration with other systems API. However, these processes and actions still need to be tested multiple times to avoid failures and comply with all applicable laws and regulations. Like any banking professional, AI systems require brand-specific positioning, training and mastery levels to generate value.

Ensuring that the financial services industry continues to lead digital transformation is key to maintaining its position as a global financial leader. However, integrating new technology into any business model can be tricky, especially when the technology is customer-facing and future growth depends on customer service. Financial services firms should take steps to avoid the above mistakes, ensure the long-term success of their AI investments, and improve the satisfaction of the customers they rely on.

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