AI in banking McKinsey

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 ML decision engines.

Deployed at scale, these decision-making capabilities powered by 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.

The layers of the AI-bank capability stack are interdependent and must work in unison to deliver value, as discussed in the first article in our series on the AI bank of the future. In our second article, we examined how AI-first banks are reimagining customer engagement to provide superior experiences across diverse bank platforms and partner ecosystems.

Also Read: gojek’s Bank Jago unveils financial services app that centres around users’ daily life

In the current article, we focus on the primary AA/ML decision-making capabilities required to understand and respond to customers’ fast-evolving needs with precision, speed, and efficiency.

Banks that leverage machine-learning models to determine in (near) real-time the best way to engage with each customer have the potential to increase value in four ways:

  • Stronger customer acquisition. Banks gain an edge by creating superior customer experiences with end-to-end automation and using advanced analytics to craft highly personalised messages at each step of the customer acquisition journey.
  • Higher customer lifetime value. Banks can increase the lifetime value of customers by engaging with them continuously and intelligently to strengthen each relationship across diverse products and services.
  • Lower operating costs. Banks can lower costs by automating as fully as possible document processing, review, and decision making, particularly in acquisition and servicing.
  • Lower credit risk. To lower credit risks, banks can adopt a more sophisticated screening of prospective customers and early detection of behaviours that signal a higher risk of default and fraud.

As banks think about how to design and build a highly flexible and fully automated decision-making layer of the AI-bank capability stack, they can benefit from organising their efforts around four interdependent elements:

  • Leveraging AA/ML models for automated, personalised decisions across the customer life cycle
  • Building and deploying AA/ML models at scale
  • Augmenting AA/ML models with what we call “edge” capabilities to reduce costs, streamline customer journeys, and enhance the overall experience
  • Building an enterprise-wide digital-marketing engine to translate insights generated in the decision-making layer into a set of coordinated messages delivered through the bank’s engagement layer

In the full report, AI powered decision making for the bank of the future, we examine each of these interdependent elements and their applications in detail.

The rapid improvement of AI-powered technologies spurs competition on speed, cost, experience, and intelligent propositions. To remain competitive, banks must engage customers with highly personalised and timely content to build loyalty.

Also Read: How startups can aid Southeast Asia’s Open Banking landscape

Personalised offers with tailored communication delivered at the right time through the customer’s preferred channel can help banks maximise the lifetime value of each customer relationship and reinforce the organisation’s market leadership.

To achieve these benefits, banks must build AI-powered decision-making capabilities fuelled by a rich mixture of internal and external data and augmented by edge technologies.

The core technology and data infrastructure required to collect and curate increasingly diverse and voluminous data sets is the topic of the next article in our series on the AI-bank capability stack.

Special thanks to Akshat Agarwal, Bangalore-based McKinsey associate partner, and Charu Singhal Mumbai-based McKinsey consultant, for co-authoring this report, as well as Milan Mitra and Yihong Wu for their contributions to this article.

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