The AI-powered bank – what impact will AI technologies have on a bank’s operating model?

Banking blog

Capabilities instilled by Artificial intelligence (AI) have the potential to radically change the way banks operate – a fact that increasingly puts AI on the executive agenda. In this blog post, we explore how existing AI applications can impact operating models of retail banks today. While there is already a large number of applications in place, the majority of these only enable innovation around the core thus instilling only limited change to the existing business. We believe, however, that banks should consider looking beyond their core to identify how AI can transform their business models, thereby unleashing additional value.

What are AI-technologies?

AI-based technologies aim at making computers copy intelligent human behaviours performing specific tasks that only humans used to be able to do. They can “read” text, “see” images, “hear” natural speech as well as organize and interpret information to make predictions based on this information. The main categories of AI-technologies are i) machine learning, ii) autonomics, iii) machine vision and iv) natural language processing.

Where is AI heading?

We expect AI to evolve substantially in the near future potentially impacting even more the way banks conduct their business and operate. Clients can expect more tailored offerings, yet need to get used to less human interaction. Client advisors’ time is freed up to focus on value adding activities thus deepening client interactions but need to accept that smarter systems will allow mass customization of advisory. AI might have the power to revolutionize banking as mass production systems and modularization have transformed the automotive industry. In its latest “Worldwide Semiannual Cognitive Artificial Intelligence Systems Spending Guide”, the Instatik Data-Center (IDC) expects that global spending on AI will more than quadruple by 2021 to $57.6bn up from $12bn in 2017 which reflects the high expectations businesses associate with these technologies.

How can AI innovate banks?

AI can enable banks to innovate in three ways: i) around their core (e.g. limited change to the known in the core business), ii) adjacent to their core (e.g. extending existing capabilities into new areas or developing new operating modes for existing processes) or iii) in a transformational way (e.g. major changes aimed at shifting the operating model to create new markets, processes or unique selling propositions).

To understand the potential AI can have for retail banks today, we looked at the vendor market for AI applications and identified vendors with applications that could have an impact on how banks operate. From 225 analysed providers globally, we determined that 102 applications are in scope for our assessment with the following distribution of capabilities (see figure 1):

Figure 1: Assessment of 102 applications

Figure 1: Assessment of 102 applications

Subsequently, we have analysed the impact these applications could have on the main steps of the retail banking value chain and which innovation “type” is addressed: core, adjacent or transformational. Figure 2 below highlights the extent to which each innovation type can be enabled by these applications for each step on the retail banking value chain:

Figure 2: Impact of applications on retail banking value chain

The way forward

Despite the perceived hype around AI, applications that can instil transformational innovation are rare. The majority of AI applications enable core and adjacent innovations focusing on increasing efficiency. It thus remains a challenge to find viable opportunities to employ AI as enablers for disruptive change in bank operating models.

To reap greater benefits from AI technologies, we believe that banks should first develop an extended understanding of AI and how it can drive business model innovation particularly in the context of open banking. The emergence of ecosystems and the greater access to data will lead to new use cases and certainly will drive innovation in relation to applications of AI. Executives who want to exploit these opportunities are therefore advised to acquire an understanding of the potentials and limitations of the technology as well as to develop a certain open-mindedness for non-traditional solutions.

A facilitator for an explorative AI feasibility assessment can be the involvement of a cross-functional team consisting of specialized AI experts, bank operating model experts as well as relevant bank-internal stakeholders who together form an expert group that is able to explore the benefits AI can unleash within a bank from a multidimensional perspective.

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This blog post originally appeared on Deloitte Switzerland’s Banking Blog and was authored by Patrik Spiller, Dr Stefan Bucherer, Jan Witt and Leon Struett.

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