The financial services sector has recently transformed into a 24/7 always on, internet-based, mobile-accessible consumer environment. With more and more of our lives taking place online and on demand, banks recognise that evolving customer expectations are forcing them to rethink their engagement strategies – and they’re looking toward the benefits that AI-based technologies might provide.
However, a new study by Pegasystems shone some light on what consumers really think about the use of AI in customer service in the UK. When focusing specifically on the banking sector, over two thirds (73%) of respondents said that they would trust a human over AI to make a completely objective, unbiased decision when deciding to give them a bank loan.
In addition, when asked who you would prefer to decide whether or not to provide you a loan –a human or AI – 81% answered a human employee at a bank, compared to 19% saying AI.
With more and more banks trying to harness the power of AI in their customer engagement, these figures show there is a long way for them to go before their customers embrace this technology in day-to-day banking.
The reality is that AI is already seamlessly integrated into our daily lives, and it’s more practical and useful than many of us realise. But a lot of consumers still can’t get past the trust issue, especially when we hear stories of banks misusing AI. So how can financial services organisations use AI now, in a way that will improve the customer experience and help win them over in the future?
Personalisation is key
It starts with getting personalisation right. There is a fine line between utilising a customer’s preferences and needs to deliver real-time, relevant information and making the person feel as though they’re being followed online. AI can be used to create a smarter, personalised user experience – it just has to be done properly. For example, tracking data (with given consent) like a customer’s spending and purchase history over the course of a few months may create an opportunity for outreach regarding budgeting and saving.
By presenting a customer with relevant information at the right times, banks can provide a better service, with the financial well-being of the customer in mind. When personalised AI is used appropriately, it increases customer satisfaction and retention, creating mutual value for the customer and the organisation.
Engage with empathy
In a world where financial services organisations purport to be customer-centric, consumers aren’t buying it. Not enough businesses show empathy towards their customers’ individual situations. For the masses to begin to trust AI, we need to incorporate empathy into our decision-making systems. The good news is that with the right controls in place, AI can do this today.
By combining machine learning to help predict customer behaviour and an ethical framework put in place by humans, organisations can determine what the next best action should be for banking customers. Whether it’s making them a relevant offer, listening to them (and remembering their intent and requirements), thanking them for being a valued customer or leaving them alone when that’s the right thing to do companies can truly engage.
This approach, though driven primarily by data and machines, is empathetic, because it considers the customers’ interests first and foremost, not just the company’s short-term profit goals. Without AI, it’s impossible to deliver this kind of empathetic customer experience on a large scale.
Customer value management
While many banks have moved past completely separate channel silos (the worst case), they are often only in a semi-connected environment. This still limits visibility into the customer’s history, preventing banks from completely understanding the context of a customer’s actions and their overall journey.
For consistent experiences, banks need an integrated enterprise system that can consolidate a customer’s data from all sources (history, channels, apps, real-time context, APIs to third-parties) and can then use AI to provide real-time recommendations to increase loyalty, retention, and value. This combination of AI and omni-channel decisioning can create massive value across the customer experience.
When a central brain is used to analyse structured and unstructured data, such as all of a customer’s interactions, organisations can use that data to create a larger view of each customer’s end-to-end journey. AI can then help produce a response that is tailored for both the customer’s immediate need and bank’s lifetime value.
Omni-channel capability allows for these messages to be delivered in the customer’s preferred channel, making channels the message bearers for relevant and timely outreach. By using data collected from customers and pairing that with predictive analytics, banks can reach customers and create value in ways they haven’t been able to before.
As people begin to expect more customisation and personalisation in their lives and products, curated financial services and advice will increase in popularity. AI is essential for knowing what services or offers to provide the customer – it allows banks to take a holistic approach to customer service. Using AI-based decisioning that is informed by customer profiles and preferences, consumer banks can dynamically package products and services together based on personalised needs. The benefits of this approach are many.
For the bank, more and better coherent products are associated with greater customer loyalty and lifetime value. On the customer’s end, there is value in the convenience of working with a trusted organisation that understands their personal needs. As the adoption of AI-based decisioning tools grow, relationship managers will be able to more accurately and consistently assist a customer with the best products and services for their financial needs.
Putting insight into action
In the banking industry, predictive models and analytics from customer data can inform decisioning processes, weighing the recommended best actions with regard to sales, service, risk, operational decisions, and customer lifecycle. They can then help banks discover meaningful patterns and engagement opportunities by anticipating customers’ needs.
For example, technologies like natural language processing (NLP) are useful in detecting patterns and anticipating customer needs. As part of an augmented CRM and decisioning system, NLP can capture content from customer interactions (e.g., emails, texts, social media posts), then the resulting data can be leveraged by decisioning and machine learning tools to recommend actions that will provide relevant and consistent customer-cantered engagement.
Similarly, AI-based decisioning tools help banks move to a more personalised, one-to-one, outcome-focused approach. The advantage of using automated decisioning and machine learning is the ability to analyse hundreds of individual customer data points in a fraction of a second to determine the optimal approach or action for the customer at that particular moment. A single, omni-channel decisioning authority can underpin the delivery of personalised customer engagement.
The Evolution of AI
When answering questions in Pega’s survey about AI decision making more generally, over half (55%) of respondents said it’s possible for AI to demonstrate bias in decision making, and 56% said it isn’t possible to develop machines that behave morally.
Furthermore, Pega’s research revealed that most customers (81%) prefer a decision about a bank loan be made by human vs. a bot, most likely because those customers have more trust in humans and believe it’s possible to influence their decisions.
But that’s not always the case, as that humans may also have an unconscious bias. In addition, when other ethical decisions are left to just humans, we’ve seen plenty of cases in banking where unsuitable products were pushed on consumers who were destined to default.
AI systems should work in conjunction with employees to ensure we make ethical decisions. With many businesses turning to AI to improve the customer experience, it’s important for organisations to understand customer perceptions, concerns, preferences, and limitations while embedding ethical considerations into machine learning systems. This will enable AI decisioning to be viewed as empathetic.
Customers aren’t going to trust banks overnight if AI is the sole arbiter of empathy in an organisation. However, by demanding responsible use of data and machine learning, AI can be used as a powerful tool to help guide decision making toward more optimal results for consumers and businesses.
The goal for AI is to enhance the technology through responsible applications, unquestionable customer convenience and ease, and improved outcomes for all. With empathy. Only then will banking customer perceptions about AI change.