Strong today, weaker tomorrow

How the rise of AI in financial services will challenge the norms of today’s banks and provide the services and insights that their customers are demanding.

The rise of artificial intelligence (AI) will have profound impacts on the way we manage our financial lives. It will alter not just how we buy financial products and services, but also change the products and services themselves.

Whilst we’ll feel this change in our personal lives, hopefully in the form of better interactions, services and options for consumers, arguably the biggest change will be witnessed by the dominant financial services institutions that have, for decades, sought to grow their businesses on foundations that could quickly be challenged by AI. Scale, physical footprint, standardised products, barriers to switching to name a few are all being challenged.

The emergence of FinTechs have seen the traditional products and services provided by banks being unbundled as the large number of start-ups, some working with banks and others going alone, have disrupted the various products offered. This product-level disruption of dominant financial institutions will be followed by a more significant disruption, the potential for structural disruption as AI-driven services become the norm in the market.

Whilst banks, and other large financial institutions have been forced to adapt their product offerings in response to the emergence of FinTech, the scale of change required to adapt to this next wave of disruption will be far more reaching.

The very thing that makes banks strong today, could be their challenge tomorrow.

Assets vs data
The strength of today’s financial behemoths can perhaps be most easily defined by the scale of their assets. Banks with significant assets benefit from economies of scale. Tomorrow’s financial leaders might be better judged by the scale of their data and how they use this more effectively.

AI can drive operational efficiency that can outweigh traditional economies of scale, but elegant AI solutions depend entirely on data to learn from.

Whilst big financial institutions are data rich already, the data they hold is unstructured and underused.

Mass-production vs tailored experiences
For decades banks have offered ‘one-size-fits-many’ financial products to consumers because standardised products enabled cost-effective revenue growth. Far better to have a suite of standard credit card products you can offer to a broad customer base, than to have to manage individual rates and offers for each customer. These kinds of bespoke products were just as hard to advertise as they were to manage.

A financial services market utilising the full potential of AI is far more likely to be dominated by tailored products and services, as the new technology enables easier distribution and management of personalised interactions.

High switching costs vs high retention benefits
Perceived high-barriers and minimal benefits of switching financial services providers has led to a well-documented consumer apathy in financial services. As a result, large financial institutions have been able to rely on high rates of customer retention without having to offer some of the benefits seen in markets like telecoms.

AI could change this by both lowering the barriers to switching and creating more opportunities for personalised loyalty benefits. Customers could be retained not through apathy, but through the continuous improvement of product performance, and the growing understanding a product provider has of an individual customer.

Human-driven performance vs augmented performance
To add or improve processes in today’s large financial institutions management is reliant on additional labour and functional training. If they want to improve something, they need people to do it. This reliance on human ingenuity has led banks to have to scale through additional resources and better training.

The growth of AI in financial services allows for augmented performance, where the interplay of strengths across technology and talent amplifies performance. This means being the best will be those that get the interplay between technology and talent right. Furthermore the financial services industry needs to attract different skills - people with data and coding skills – skills that are in hot demand in all industries.

These changes will have far-reaching consequences for the make-up of financial services; placing legacy business models under pressure from those that have built business on the foundations of success in AI enabled services.

The cultural change required from dominant financial services institutions cannot be underestimated. Arguably, this will be the biggest challenge as “corporate anti-bodies” struggle with the level of innovation and change required. Some of the most difficult of these will be the adoption of change that will challenge the successful status quo of such organisations. 

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