Artificial intelligence (AI) is bringing retail banking its deepest level of disruption in decades, if not ever. Mohamed Dabo looks at a new report by McKinsey & Company that outlines the path to the AI bank of the future

After years of incremental change, banks must plan for a fundamental rethink of operations in order to thrive in a rapidly digitised and data-driven world.

“The advancement of AI technologies within financial services offers banks the potential to increase revenue at lower cost by engaging and serving customers in radically new ways, using a new business model we call the AI bank of the future,” the report reads.

AI-enabled banks of tomorrow will be key to solving banking’s major challenges, such as fraud, customer experience, security, operations, and financial forecasting.

Over the last six months, McKinsey has been developing an extensive new perspective on the artificial intelligence-driven Bank of the Future, which it has published for the first time in the report.

The five chapters of the report explore the key milestones for banks seeking to leverage the full power of artificial intelligence to achieve deeper customer relationships, expanded market share, and stronger financial performance.

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1. AI-bank of the future: Can banks meet the AI challenge?

For many banks, ensuring adoption of AI technologies across the enterprise is no longer a choice, but a strategic imperative.

Envisioning and building the bank’s capabilities holistically across four layers will be critical to success.

Four questions reflecting these layers can help leaders articulate a clear vision and develop a road map for becoming an AI-first bank:

  • Why must banks become AI-first?

Banks that fail to make AI central to their core strategy and operations—what McKinsey refers to as becoming “AI-first”—will risk being overtaken by competition and deserted by their customers.

  • What might the AI bank of the future look like?

Internally, the AI-first institution will be optimised for operational efficiency through extreme automation of manual tasks (a “zero-ops” mindset) and the replacement or augmentation of human decisions by advanced diagnostic engines in diverse areas of bank operations.

The AI-first bank of the future will also enjoy the speed and agility that today characterize digital-native companies.

  • What obstacles prevent banks from deploying AI capabilities at scale?

Despite billions of dollars spent on change-the-bank technology initiatives each year, few banks have succeeded in diffusing and scaling AI technologies throughout the organization.

Among the obstacles hampering banks’ efforts, the most common is the lack of a clear strategy for AI.

Two additional challenges for many banks are, first, a weak core technology and data backbone and, second, an outmoded operating model and talent strategy.

  • How can banks transform to become AI-first?

To overcome the challenges that limit organization-wide deployment of AI technologies, banks must take a holistic approach.

To become AI-first, banks must invest in transforming capabilities across all four layers of the integrated capability stack: the engagement layer, the AI-powered decisioning layer, the core technology and data layer, and the operating model.

2. Reimagining customer engagement for the AI bank of the future

Superior experiences are not only a proven foundation for growth but also a crucial means of countering threats from new attackers.

In particular, three trends make it imperative for banks to improve customer engagement:

  • Rising customer expectations

Accustomed to the service standards set by consumer internet companies, today’s customers have come to expect the same degree of consistency, convenience, and personalization from their financial-services institutions.

  • Disintermediation

Nonbank providers are disintermediating banks from the most valuable services, leaving less profitable links in the value chain to traditional banks.

Big-tech companies are providing access to financial products within their nonbanking ecosystems.

Beyond access, nonbank innovators are also disintermediating parts of the value chain that were once considered core capabilities of financial institutions, including underwriting.

  • Increasingly human-like formats

Conversational interfaces are becoming the new standard for customer engagement.

For banks, successfully integrating core personalization elements across the range of touchpoints with customers will be critical to deliver a superior experience and better outcomes.

The reimagined engagement layer should provide the AI bank with a deeper and more accurate understanding of each customer’s context, behaviour, needs, and preferences.

3. AI-powered decision making for the bank of the future

The ongoing transition to digital channels creates an opportunity for banks to serve more customers, expand market share, and increase revenue at lower cost.

Crucially, banks that pursue this opportunity also can access the bigger, richer data sets required to fuel advanced-analytics (AA) and machine-learning (ML) decision engines.

Deployed at scale, these decision-making capabilities powered by artificial intelligence (AI) can give the bank a decisive competitive edge by generating significant incremental value for customers, partners, and the bank.

Banks that aim to compete in global and regional markets increasingly influenced by digital ecosystems will need a well-rounded AI-and-analytics capability stack comprising four main layers: reimagined engagement, AI-powered decision making, core technology and data infrastructure, and a leading-edge operating model.

4. Beyond digital transformations: Modernizing core technology for the AI bank of the future

Before embarking on a fundamental transformation of core technology and data infrastructure, financial-services organisations should craft a detailed strategy for building an AI-first value proposition.

They should also develop a road map for the transformation, focusing on three dimensions of value creation.

These include faster time to market with efficient governance and productivity tracking; clear alignment of demand and capacity to meet strategic and near-term priorities; and a well-defined mechanism to coordinate “change the bank” and “run the bank” initiatives according to their potential to generate value.

5. Platform operating model for the AI bank of the future

The need to change a bank’s operating model arises from a combination of external and internal circumstances.

Externally, as consumers and businesses increasingly rely on AI technologies in daily life, banks are shifting the foundation of their business models from products to experiences. In other words, as many traditional banking products become embedded—or even “invisible”—within beyond-the-bank journeys, experiences become the more salient element of a customer’s relationship with the bank.

This shift involves a rapid increase in the number of customer interactions, and at the same time, the revenue associated with each interaction is declining.

This is a fundamental change: just a few years ago, customers conducted business with the bank by visiting a branch once or twice a month; more recently, they would conduct transactions several times each week through the bank website; now many customers interact with their bank daily through their mobile banking app, and often several times a day through wearable devices.

In short, banks and their customers now have an interconnected, always-on relationship.