Business Transformation

AI automates workflows, but seamless CX still requires the human touch

It’s essential to align consistent CX to customer needs and preferences as you grow. AI guides interactions and crunches data, but humans will always play critical roles.

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If we thought 2023 might bring calmer conditions after the global economic instability and upheaval of 2022, we would have been right in some respects. Although inflation and interest rates remain high, energy and gas prices have returned to normal levels. While the macroeconomic situation is stabilising to some degree, funding in the fintech world continues its freefall, dropping by 49% year over year in the first half of 2023. But investment in one area is frenetic.


It’s fair to say that the speed of AI’s progress in such a short space of time has created ‘FOMO’ – fear of missing out – in the fintech industry. Companies everywhere are scrambling to make meaningful investments in AI while many are still dealing with tighter budgets. The question now is: how to harness AI and use it to improve customer experience?


When done well, CX can be your biggest competitive differentiator. It can be the most effective part of your toolbox to nurture deeper and long-lasting loyalty. So, how can AI be used to implement CX that’s customer-centric, data-driven, and informed by accurate customer feedback loops?


Developments in AI and its use in CX have been performing with mixed results for quite a few years now. At its best, AI powers chatbots to answer basic questions such as account balances and intelligently route more complicated issues to live agents for quick resolution.


Increasingly, AI also drives better identification of cost reduction areas by automating simple or repetitive customer service processes hidden from customers’ view. Take customer onboarding for instance. Fortunately for many busy in-house fintech teams, AI doesn’t mind sifting through reams of paper or digitally scanned documentation, and it’s accelerated what was previously a laboriously slow and error-ridden workflow into something much faster, smoother, and highly accurate. AI can use natural language processing to speed-read documents, verify whether they’re fake or genuine, and cross-reference them with other sources to ascertain authenticity.


With much faster and efficient onboarding, customers can open accounts in just a few minutes, start using their cards or accounts straight away. That’s one way AI can make an improvement in CX.

Dynamic data analytics can drive deeper loyalty

With fast-flowing customer and transaction data coming from an expanding array of touchpoints and sources, fintechs need to filter this data quickly and effectively to unlock the customer behaviour insights they need to incentivise cross-selling and deeper loyalty. AI can consume and map massive amounts of data from multiple sources, and transform it into valuable business intelligence that enables companies to get closer to their customers. Empowered by those insights, fintechs can design much more personalised products and services.


Right now, firms like Klarna, Stripe, Fiserv and others are using generative AI like ChatGPT to mine transaction histories, social media interactions, and customer preferences to predict future behaviour. For example, Klarna’s ChatGPT plugin enables shoppers to ask for tips on what product to buy, and recommends items from Klarna’s network of 500,000 merchants. By doing so, Klarna is helping customers find the right products, improving this aspect of CX and sales even further.

Compelling customer service interactions

From chatbots to robo-advisors to virtual assistants, AI is already enhancing customer service capabilities. Specifically, AI can revolutionise the quality and training aspects of customer service by applying speech recognition and natural language processing to customer interaction data across channels. This empowers businesses to identify exceptional and below-average customer interactions and spot contact behaviour and preference trends.


AI also plays a crucial role in analysing agent behaviour during customer interactions, providing real-time feedback to enhance performance, reduce handling time, improve compliance and improve customer satisfaction. It can serve as a digital assistant or supervisor for agents, helping them make every interaction quicker, more informed, and compliant.


It’s important, however, to distinguish between generative AI and conversational AI and how they relate to CX. Generative AI is already showing impressive potential to take chatbots to a whole new level of sophistication and personalisation. Generative AI refers to the ability of AI systems to generate original and coherent content, such as generating written text or other media formats like speech and images. These can be incredibly useful in creating personalised responses or generating new dialogue for chatbots.


On the other hand, conversational AI focuses on the interaction between AI systems and humans, with the goal of creating natural and engaging conversations. By using natural language processing, AI systems can understand and respond to human queries and prompts in a conversational manner. The combination of generative and conversational AI will be a game-changer in providing seamless and human-like interactions, enhancing customer experiences, and improving overall customer satisfaction.


Businesses can identify performance patterns that can train AI systems to engage in meaningful conversations with customers, improving the overall quality of customer interactions. When customers feel listened to, they feel valued, and will report much greater levels of satisfaction, which in turn can. enhance customer recommendations and word-of-mouth advertising.


Working towards improving your Customer Effort Score could help you identify whether your AI and customer support is functioning as it should to resolve customer issues quickly and with the lowest effort required on their part.


The Customer Effort Score is recognised as being important to overall satisfaction. This score measures how much effort a customer has to put into getting a question answered or a problem resolved. It’s typically a simple question – such as “Did we solve your problem today?” – that’s asked in a post post-transaction survey and measured on a scale of 1-7. It’s not as widely used as popular measures like the Customer Satisfaction Score (CSAT) or Net Promoter Score (NPS), but the trouble with both metrics is that they’re limited in what they can tell you. Normally, only a sliver of your customer base responds to these surveys.


While CSAT and NPS are still important metrics to track, the Customer Effort Score can complement them and also give you more insight into your entire customer journey. And as you deploy AI, it’s critical to measure your success. At its best AI can speed up issue resolution, improve CX, and create happier customers, who will become your most effective brand advocates.

What will tomorrow’s AI-driven CX strategies look like?

Right now, AI can automate simple manual tasks and enquiries, enhance business analytics and is proving effective in helping customer support staff and other departments improve productivity. But the technology is still at an early stage of development and is still getting to grips with the many nuances of customer interactions. When customers come up against complicated or sensitive problems, can an algorithm really give them the personalised and intelligent decisions to make the right choices? AI is a helpful tool to guide customer service staff, but for more complicated issues that touch on compliance – lending decisions or when a customer runs into financial difficulties, for example – an AI algorithm can’t yet explain why it made a decision.


The problem of AI hallucinations – a confident response by an AI that does not seem to be justified by the data it gathers from elsewhere – alongside incomplete data, and potential bias, could leave a bank or fintech exposed to possible accusations of discrimination, errors, or unfair judgement. As AI learns and becomes more advanced, it offers huge potential to improve business efficiencies – but it also needs humans to help it work better.


When incorporating AI into your CX processes, it’s essential to evaluate the agent experience. If the technology is distracting or frustrating, it could cause more problems than it solves. The same principles apply to other self-service tools, like chatbots, which can be helpful for dealing with basic enquiries but can struggle with more complex tasks.


As fintechs scale, there is a risk that CX strategies can get stretched and break down, meaning the customer’s motivations and behaviour fade from view. An approach that works well in one contact channel may be detrimental in another. As your business expands, it’s essential to align consistent CX to customer needs and preferences. Companies with high-performing CX platforms are 7.9 times more likely to unify their data across their organisation in order to get a single, unified view of customers’ journeys.


At Ubiquity, ultimately, we see AI as a positive catalyst for improving internal processes and fostering deeper customer relationships. Customers are warming to the idea of AI, with 77% finding chatbots helpful for simple issues, and 71% agreeing that AI and chatbots can help provide faster replies. A CX approach, blending both conversational and generative AI, can analyse past as well as real-time customer interactions for much better impact. By surfacing meaningful topics and processing patterns, agents can better help customers with their issues, and customers can better understand how their issues were solved.


Right now, AI is not quite ready to be out there on its own, particularly when it comes to more complicated and sensitive areas like dispute or fraud management. But I do believe that over time, AI can be harnessed in the fintech world to give exceptional CX that customers will love – and which will unlock surefire revenue growth for ambitious fintechs.



Find out how Ubiquity's CX specialists optimize customer experience programs, whether with AI-enabled agent guidance or AI-powered data analytics.

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