AI in Banking

What are the competitive advantages?

João Jarego, Closer Consulting

Widely publicized events like, in 2016, the defeat of the 18-time world champion Lee Sedol at the game of Go (known for its complexity and requiring intuition, imagination, and strategic thinking) by AlphaGo, or five years before, the victory of IBM Watson at Jeopardy, promoted a never seen before awareness of the power of artificial intelligence (AI) technologies. The scandal of Cambridge Analytica added a darker note to it, but still added to this awareness. Today, few would disagree that we now live in the AI-powered digital age.

Over the last decades, banks have continually adopted the latest technology innovations. By doing so, the way customers interact with them was redefined: ATMs were introduced in the 1960s; electronic, card-based payments in the 1970s; the early 2000s saw the widespread adoption of 24/7 online banking; the mobile-based “banking on the go” followed with the rise of the smartphones. Yet, when it comes to AI, Banks have struggled with the transition to becoming “AI-first” organizations and going beyond experimenting with selected use cases.

The reasons for that include inflexible and often outdated core systems, fragmented data assets, operating models where business and technology cooperate insufficiently and at last, but not the least, the lack of a clear strategy for AI. Adding to this, Big Tech (and “not so big”) companies are paving their way into financial services, bringing added competition to the sector. On their road to AI, Banks also have to deal with a scarcity of talent, which is easily attracted by tech companies’ more thrilling pace of innovation (namely startups).

Why should Banks embrace AI? Banks face these ongoing trends:

  1. Increased customer expectations towards banking digitalization. The pandemic saw increased adoption of online and mobile channels, a change that will not, according to different surveys, be reverted with the end of the sanitary crisis.
  2. Increased use of AI by Leading financial institutions, which will set new standards for the industry by taking a comprehensive approach to deploying advanced AI, and deploying it to the front and back-office systems. This will broaden the gap between the Banks lagging on their AI initiatives and losing ground to their more “sophisticated” competition.
  3. Disintermediation of the traditional financial services either by its inclusion in digital ecosystems that provide seamless access to a broad range of services or by blockchain based technology.
  4. Big Tech’s appetite for financial services, where they can leverage their market advantages: immense AI capacity as this is their core; huge and engaged customer network, which generates a plethora of data that allows a very precise understanding of each of its individual customers. Furthermore, they have access to low-cost capital. Everything considered, a greater footprint of the Big Tech in this industry should surprise no one: they are, indeed, the most convincing “apostles” of Bill Gates’s 1994 provocative prophecy: “banking is necessary, but banks are not.

What competitive edge can Banks get with the effective adoption of AI? Benefits range improved cost-to-income, shortened innovation cycles, better and large-scale personalization, and an enhanced omnichannel experience. Being more specific, we address how Banks can take advantage of AI to induce cost reduction, income growth and improvement of the user experience and engagement (which is closely related to the income, as we will see).

  • Regarding costs, Banks have huge back-offices with still too much human intervention, which slows the processes and makes them costlier. AI can help in a multitude of ways: either by redesigning/simplifying the processes themselves, an intelligent and real-time distribution of tasks that optimizes efficiency, or automating dozens of steps/decisions in the Bank workflows (both by full replacement or augmentation of human judgment to produce significantly better outcomes). The financial services newcomers will not carry the legacy of pre-existing processes, and these will be designed to be as lean, automated and, ultimately, cheap as possible. Still from the cost perspective, only AI can provide Banks the tools to effectively fight fraud, which with the increased adoption of CNP (Card not Present) payments and online banking, offer new opportunities to the fraudster’s endless creativity and growing challenges to Banks and payment processors.
  • Revenue will grow if Banks are able to find better answers to questions like: “what is the best financial service for this particular client? What is the right time and the right channel for the contact?” Unlike companies such as Amazon, which have honed their targeting skill to the point where they sometimes anticipate customer needs before the customer is even aware of them, Banks’ recommendations too often lack relevance and, therefore, have a low hit ratio. AI also offers the opportunity of growing market share through insights on which financial products a client may be using from the competition and offering a more attractive alternative (e.g. a consumer lending with a different Bank).
  • The importance of User Experience cannot be stressed highly enough. Yet, Banks’ performance indicators do not consider the core KPI for internet companies: user experience and engagement. Research has shown that the better the experience and the more satisfied the customer, the more likely the Bank will generate higher revenue: a more satisfied customer accounts, typically, for approximately 2.4 times more revenue than a neutral customer[1]. Our own experience at Closer shows that the more interactions the client has with the Bank, the less likely is this client to churn. For the Bank to be ubiquitous in customers’ lives, it must be able to deal with latent and emerging needs while delivering intuitive omnichannel experiences and several key shifts need to be undertaken. To begin, Banks need to move beyond pushing highly standardized products to integrate propositions that are customer-centric. These can no longer be static and should go beyond banking to include non-banking products and services. Banks should work on simplifying customers banking activities and provide actionable insights on how to manage their financial lives, e.g.:
    • Fee-reduction recommendations: among these, identification of duplicate services or high bills suggesting actions like more competitive options or reducing the number of overlapping subscriptions or even recommending options to reduce bank fees (although at a first glance this may seem contrary to the Bank own interest, the customer loyalty outshines the marginal potential decrease in charged fees).
    • Budgeting tools have been around for a long time, but the main reason why they have not seen widespread adoption is that they do not provide practical answers to practical questions and concerns. Having alerts like “You have spent 75 percent of your clothing limit”), reminders like “You paid the electricity bill on the 5th last month. Would you like to pay now?”, or planning aids like “If I buy this dress, what would be the expected balance of my current account on the end of the month?” would impact people’s lives in a very meaningful way. Regarding the savings, proposing tailor-made investment plans according to surveyed life goals like buying a house in 5 years or early retirement at 50 would also be extremely relevant.

A lot needs to be done under the hood to tackle these challenges. Examples include:

  • Integration of a range of data from multiple sources both within the bank (e.g., clickstream data generated in apps) and outside the bank (third-party partnerships).
  • Data platforms that aggregate a 360-degree view of customers and enable AI models to run and execute in near real-time.
  • Campaign platforms that track past actions and deliver and monitor interventions across the complete range of channels in the engagement layer.

The road is long, so a clear strategy and a well-defined roadmap are essential requirements. But, at the same time, avoid the analysis-paralysis and start deploying actions as this is an interactive process. Actions will, besides their intrinsic value, add clarity to the plan.

[1] Peter Kriss, “The Value of Customer Experience—Quantified,” August 1, 2014,


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