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.
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. In the current article, we focus on the primary AA/ML decisioning 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 potential to increase value 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 personalized 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 more sophisticated screening of prospective customers and early detection of behaviors that signal higher risk of default and fraud.
Learn more about the bank of the future
As banks think about how to design and build a highly flexible and fully automated decisioning layer of the AI-bank capability stack, they can benefit from organizing their efforts around four interdependent elements: (1) leveraging AA/ML models for automated, personalized decisions across the customer life cycle; (2) building and deploying AA/ML models at scale; (3) augmenting AA/ML models with what we call “edge” capabilities1 to reduce costs, streamline customer journeys, and enhance the overall experience; and (4) 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, downloadable here, 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 personalized and timely content to build loyalty. Personalized offers with tailored communication delivered at the right time through the customer’s preferred channel can help banks maximize the lifetime value of each customer relationship and reinforce the organization’s market leadership. To achieve these benefits, banks must build AI-powered decisioning capabilities fueled 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.
Download the full report here.
