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Conversational AI in banking – 3 common mistakes that businesses make

by uma


By: Faisal Abbasi, Managing Director UK & Ireland, Europe, Amelia

The Financial Services (FinServ) industry is often perceived to be rather stuck in its ways compared to others, operating with legacy systems that are still critical for certain financial processes. However, when it comes to digitisation, it is actually leading the way in implementing transformation plans —research shows that the FinServ industry is one of the most digitally mature sectors, with a high 28% success rate when it comes to digital transformation plans in comparison to other industries. 

Emerging competition, and a changing world of work, has catalysed the adoption of modern technologies across the entire ecosystem of FinServ organisations. Adoption and successful implementation, however, are two different things, and mistakes are being made that are restricting the benefits that these organisations can gain from new technology. Conversational AI in customer experience is one that many major global banks have adopted but yet to use to its full potential.

Banks have created proprietary chatbots to deal with simple customer queries or working with vendors to install bots on their websites. Whilst these can serve a useful purpose, they come with limitations, primarily because not every bot is created equal. The level of conversational prowess and ability to process information, and ultimately provide customers with adequate solutions, varies massively between chatbots.

Research into customer experiences with chatbots sees a common gripe crop up time and time again – 37% of people feel that chatbots often simply run out of steam, with their limited, pre-programmed answersoften meaning that they’re unable to get to the crux of the problem. In truth, the majority of modern systems being installed offer very little cognitive intelligence, little to no automation and are capped in their ability to handle customer issues, often only able to provide stock answers to frequently asked questions.

So how dofinancial businesses get it right when it comes to implementing Conversational AI?As a start,  here are the three most common mistakes that banks need to avoid when deploying these systems.

Putting shareholders over customers

Every bank wants to save money by automating basic customer-employee interactions but when considering a Conversational AI implementation, if the end-goal fails to help customers accomplish their goals more quickly than traditional customer support methods, the project should be re-examined before it even begins.

Customers are not aware, or frankly don’t care, about a bank’s bot’s limitations. If they receive an answer to their initial question, they’ll likely ask a more complicated follow-up or ask if they can make a transaction. A basic chatbot will field those follow-up questions in the only way it knows how —by escalating them to human operators. In the end, users have poor experiences, human intervention is still required (the avoidance of which was one goal of using a bot in the first place) and customers are likely to turn back to time-consuming human contact methods in the future, rather than rely on an ineffective bot. In essence, an organisation has invested in a project that drives customers toward experiences that they don’t want or need. 

Not choosing the right tools for the job

If a bank has truly decided to digitally transform, it should investigate more advanced Conversational AI solutions that will provide higher levels of investment protection and effectiveness, compared to deploying a simpler chatbot that will quickly become obsolete.  Investments need to be as future-proof as possible and Conversational AI agents are skilled enough to execute tasks based on expert, data-based decision-making, and then learn and anticipate new scenarios from those interactions over time to remain current with customers’ needs. To get started, banks should identify processes that are high volume and apply to common business issues. In other words, they should target the most frequently occurring or repeated customer issues, ones for which an advanced AI solution can deliver results without human interaction. Conversational AI systems are most valuable, particularly in the short-term, at helping businesses improve customer inquiry response rates, handle times and first-touch resolutions, as well as locating the appropriate human worker to complete a process that can’t be resolved with automation.

For example, customers who ask questions such as, “Should I apply for a small business loan?” or “Should I cash out my ISA?” are not looking for a generic scripted answer. With a cognitive system in place, a bank can leverage machine learning, dialogue variance and historical memory to provide informed opinions about customers’ questions and concerns. The system can research consumers’ banking history, access market data, perform calculations and, most importantly, inquire about their financial goals in order to render educated recommendations.

Deploying too quickly

Practice makes perfect, even for digital workers. Cast a wary eye toward any vendor that tells you that its AI system can plug into your existing IT ecosystem and be customer-ready within a few hours. Installing a Conversational AI banking solution and training it to achieve your end goals are vastly different, albeit connected, scenarios.

With continuing advances in Conversational AI solutions, banks can find solutions that follow strict banking processes, have an expert understanding of banking terminology, and offer APIs that integrate neatly with other systems. However, organisations still need to test —and test and test —each of these processes and actions in order to avoid failure and remain in compliance with all applicable laws and regulations.   As with any human banking expert, an AI system requires levels of brand-specific orientation, training, and mastery in order to generate value.

Ensuring that the financial services industry continues to take the lead in driving digital transformation is key to the UK maintaining its position as a global finance leader. However, incorporating new technologies into any business model can be tricky, especially when that tech is customer-facing and future growth depends on strong customer service. FS firms should take measures to avoid the mistakes mentioned above, ensuring that their AI investment will be a long-term success and provide the customer satisfaction that they rely on.


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