How banks are using digital assistants to meet their challenges head-on
It’s safe to say that the banking sector is one of the most disrupted right now, with a myriad of challenges to address.
The banking landscape has changed with new competition from FinTechs, alternative business models, increased regulation and compliance pressures, not to mention, the rapid emergence of disruptive technologies.
The grand banking institutions are having to rethink the way they do business. This is largely due to the way customers want to interact with their preferred finance partner. Customer demands are evolving quickly, as consumers seek 24/7 personalised service and support.
This coupled with reducing operational costs and real estate, banks are investing in Conversational AI, using digital assistants to automate customer contact across multiple communication channels. What we see in the market, however, is the disparity between individual banks’ capabilities and understanding of how to develop a digital assistant platform that universally addresses key requirements such as; personalised customer support, an accelerated path to resolutions, and proactive customer engagements.
Although banks have similar ambitions, the levels of customer request automation are generally lower than anticipated. One of the fundamental blockers is the ability to understand what a customer is asking. Training and testing the understanding engine (NLP) is a critical factor in solving this. Typically, this is very manual which inhibits the optimisation of training data to realise the full potential of the technology. A lack of understanding around what’s valuable and what’s not is another major challenge. Many are simply unable to make well-informed decisions.
All the banks we are speaking to want to smooth out their customer journeys to deliver the best experience and they understand the need for improvement, as well as savings on time and effort.
One bank we see ahead of the game is NatWest Group. This is backed up in NatWest Group’s H1 results, which highlighted a significant milestone, as its digital assistant, ‘Cora’ completed 35 million customer conversations since its inception in 2017. 5.3 million customer conversations were completed in the first six months of 2022 with over 49% of those conversations requiring no human intervention, fully handled by Cora alone. That’s millions of human minutes reinvested to support complex customer needs.
Those early adopter banks have moved their investment in conversation AI from a side project to an integrated business-critical function. With this, we see a move to simplify and automate a number of aspects of the development cycle. Equal investment on retraining existing talent with AI/ML skills and building a new pool of talent with the necessary skills is key. Partnering with innovative companies and new players like QBox and accelerating the journey is key, as no one can build everything themselves.
Traditional banks are also grappling with legacy technology that is convoluted and outdated. There is a desire to transition to innovative solutions but this is not as easy as it sounds. Having a defined strategy for digital assistant development needs to be looked at holistically. Too often we come across banks whose digital assistant development teams are siloed and disconnected from the mainstream business. Choosing the right NLP engine is not going to address many of the challenges I’ve already highlighted.
In times where banking customers are facing economic pressures not felt in a decade, the ability to offer high quality, lower cost service and support has never been more essential. Therefore, the ability to rapidly scale digital assistant capabilities, improve performance and automate an increasing volume of calls with a lower cost to serve are the main drivers.
We are delighted to be working with enlightened banks that understand that people with the right tools such as QBox improve their chances of success.