In banking, AI models need to be implemented in way that builds trust, improves loyalty, and moves beyond efficiency to foster meaningful connections between banks and their customers.

How AI helps meet customer needs

AI is already enabling personalisation in banking with advanced data analytics and machine learning, with some using AI to provide tailored financial advice based on individual spending patterns, create customised product recommendations and make earlier predictions of customer needs.

Trust is a hurdle

Trust is the biggest hurdle from a customer perspective. You can design for trust, with transparency being the key. Being open about AI governance policy, and where AI is and isn’t being used, can help build trust. Concerns about data privacy and algorithmic biases remain. A “human in charge” of the process is necessary, AI won’t be running branches just yet.

AI is already a driving force in transforming customer experiences, as long as security, privacy and governance is properly managed from the outset.

As we look closely at the role of AI, it's important to consider both the potential for deeper connections and the boundaries that must be respected. The banking sector will need to fully understand how AI can push the limits of personalisation without overstepping into areas that customers may find intrusive. 

Going further

Advanced AI models can do more than some of the examples we’ve already covered. Models can provide contextually relevant financial advice based on life events, understand complex financial journeys and create predictive financial wellness recommendations – extremely relevant considering the current financial landscape for many people in the UK.

Consider an AI model developed to detect a customer’s recent job change through spending patterns, which proactively suggests revised budgeting strategies or offers tailored savings and investment options. Done in a targeted way these actions will improve the customer journey, and provide actionable suggestions to customers.

So, how can AI help customers who are facing financial hardship?

Tools that can identify potential financial challenges before they occur, and suggest preventative financial strategies, can build trust and make customers feel valued and looked after.

If these touch points are personalised, easy to use across channels, they are likely to improve the customer journey and strengthen loyalty.

As more widespread AI use cases come online, formalised regulation will increase. This may be of benefit to the sector as additional customer protection may increase the acceptance of AI’s use with customer-facing interactions. Legislation like the EU AI act is coming too, so having the correct guardrails and governance will provide a solid foundation for further regulation.

Systems need to be in place to ensure there is continuous learning from customer interactions.

This will avoid interactions becoming generic and collect data accurately and securely. The aim should be to build on those interactions, so personalisation doesn’t feel like an algorithm to the customer, rather a trusted source of financial advice.

Using AI to provide timely, relevant guidance that respects personal customer boundaries can help empower financial decision making and so again, help improve that all-important customer journey and boost loyalty.

According to CASS (Current Account Switch Service), 300,000 customers switch bank accounts in Q3 2023. So to reduce churn, effective customer personalisation strategies are key.

Those who don’t successfully service this demand and deliver an excellent level of customer experience are almost certain to lose competitive advantage to those that do.

You can learn more about AI adoption by downloading our recent eBook successful ai implementation in business – what are the key considerations?