South Africa’s financial institutions are competing against fast and agile fintechs for customer retention and engagement. When onboarding takes only a few minutes and every process has been digitised and fine-tuned to optimise the customer experience, users are opting out of the traditional banking ecosystem and into platforms that gratify their needs of immediacy and accessibility. These legacy systems, built for an entirely different market and environment, are straining to meet the demands of digital transformation and the rising tide of regulatory compliance. And AI, full of promises to solve all these problems, is landing in companies that aren’t ready for it just yet.
As a result, says Andre Wissler, Strategic Digital Architect at Mint Group, there is a widening gap between what financial institutions want to deliver and what their back-end infrastructure can realistically support. “The front-end experience is increasingly agile and accessible,” he says. “The problem is the back end, where systems aren’t talking to one another and manual processes still hinder progress and timelines.”
The key is to find a way of integrating technology stacks with a centralised technology platform that allows customers and institutions to connect more effectively. Trevor Thackwell, Account Executive at Mint Group, puts it more bluntly. “This challenge is especially visible in compliance and operational servicing processes, where institutions still rely heavily on manual workflows. Large portions of these processes can be automated, reducing the load on the human in the loop without compromising accuracy.”
This comes down to AI and solutions that are built specifically around the needs of the financial services industry. Today, these solutions are built around specific use cases, designed to scrutinise inbound e-mails, classify them, route them and interpret the attachments. “Getting the inbound process right can fundamentally speed up compliance management for the financial institution.”
However, this is where the conversation becomes challenging as AI investment needs to be a long game, one that is strategically planned and implemented realistically. Seven years ago, when the industry scrambled to add chatbots to their customer service experience, many projects failed because the chatbots were dropped onto broken back-end processes. “You were adding another channel but not actually improving the customer service experience. And this lesson mirrors the AI adoption risks today,” says Thackwell.
Wissler agrees: “If you put AI on top of any broken process, the process remains broken – AI simply accelerates the failure. Investing in AI requires an old-fashioned approach – business process analysis. Define it, analyse it, fine tune it, and only then lay the AI automation layer on top of it.”
Another challenge that tends to derail AI projects is the data. It’s easy to dive into an exciting AI use case, but if data isn’t accessible, secure, up to date or relevant, the project will fail. Data needs to sit at the forefront of the conversation to ensure that AI has access to the right information at the right time.
Finally, there is the need for change management, an often-overlooked dimension of AI integration. “If you want your AI use cases to deliver, you need the right levels of user adoption and understanding. You won’t see the value of your significant AI investment if you don’t invest in change management at the same time.”
This is what sets Mint apart. “We lead with business outcomes, not product demos and technology. We partner with our clients as strategic advisors focusing on what your financial institution wants to achieve, not on dropping a piece of tech on top of your legacy architecture,” concludes Wissler. Mint is a Microsoft Inner Circle partner with a deep understanding of the financial services sector and teams that know two very important things when it comes to successful AI integration – when not to build, and how to extract real value.
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