Despite the common thrashing German, Austrian, and Swiss banks often get at conferences relevant to the topic; they do put a significant amount of effort into adopting AI for meaningful use cases. However, as with all innovations, the financial industry is still dealing with some pain points concerning AI.
1. Digital Washing – Chatbots are no AI
Amongst the influx of buzzwords and the inherent need for cool branding, many banking institutions invested large sums in their own start-up hubs, spin-off Fintech companies, and digital products that are visible to the customers. However, there is a big difference between machine learning applications, such as chatbots and actual complex AI. To cite Mat Veloso:
„Difference between machine learning and AI: If it is written in Python, it’s probably machine learning If it is written in PowerPoint, it’s probably AI.“
2. Customer-orientated vs. Profit-orientated
More than anything, this is an attitude issue. If you want to implement AI in your bank, start asking yourself why. If the answer isn’t: “Ultimately making my clients’ lives easier” – forget it. This is a problem we often face with our clients, who want to deploy sophisticated tools at all costs, forgetting the underlying motivation in the process. In many banks, especially if we compare traditional houses vs. challengers, this is a major problem in thinking and company culture. If you put a bunch of bankers in a room and ask them whether banks should be more customer or profit-orientated, you will get a very mixed response.