Over several decades, banks have continually adopted the latest technology innovations to redefine how customers interact with them. In Zimbabwe the first visible form of electronic innovation was in the early 1990s when Standard Chartered Bank and Central Africa Building Society (CABS) installed Automated Teller Machines (ATM)s. Due to the cash-crisis the adoption of 24/7 online/internet banking, followed by the spread of mobile-based “banking on the go”.
In 2016, AlphaGo, a machine, defeated 18-time world champion Lee Sedol at the game of Go, a complex board game requiring intuition, imagination, and strategic thinking abilities long considered distinctly human. Since then, artificial intelligence (AI) technologies have advanced even further, and their transformative impact is increasingly evident across industries. AI-powered machines are tailoring recommendations of digital content to individual tastes and preferences, designing clothing lines for fashion retailers, and even beginning to surpass experienced doctors in detecting signs of cancer.
The ongoing transition to digital channels creates an opportunity for banks to serve more customers, expand market share, and increase revenue at a 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.
Many banks, however, have struggled to move from experimentation to select use cases to scaling AI technologies across the organization. Reasons include the lack of a clear strategy for AI, an inflexible and investment-starved technology core, fragmented data assets, and outmoded operating models that hamper collaboration between business and technology teams.
Several trends in digital engagement have accelerated during the COVID-19 pandemic, and big-tech companies are looking to enter financial services as the next adjacency. To compete successfully and thrive, banks must become “AI-first” institutions, adopting AI technologies as the foundation for new value propositions and distinctive customer experiences.
More broadly, disruptive AI technologies can dramatically improve banks’ ability to achieve four key outcomes: higher profits, at-scale personalization, distinctive omnichannel experiences, and rapid innovation cycles. Banks that fail to make AI central to their core strategy and operations what we refer to as becoming “AI-first”—will risk being overtaken by competition and deserted by their customers.
To meet customers’ rising expectations and beat competitive threats in the AI-powered digital era, the AI-first bank will offer propositions and experiences that are intelligent (that is, recommending actions, anticipating and automating key decisions or tasks), personalized (that is, relevant and timely, and based on a detailed understanding of customers’ past behaviour and context), and truly omni-channel (seamlessly spanning the physical and online contexts across multiple devices, and delivering a consistent experience) and that blend banking capability with relevant products and services beyond banking.
Banks face two sets of objectives, which at first glance appear to be at odds.
On the one hand, banks need to achieve the speed, agility, and flexibility innate to a fintech. On the other, they must continue managing the scale, security standards, and regulatory requirements of a traditional financial-services enterprise.
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, weak core technology and data backbone and, second, an outmoded operating model and talent strategy.
Built for stability, banks’ core technology systems have performed very poor for some banks in Zimbabwe especially when they do upgrades to the system. The updates at most times fail to support traditional payments and lending operations. These systems often lack the capacity and flexibility required to support the variable computing requirements, data-processing needs, and real-time analysis that closed-loop AI applications require.
However, banks must resolve several weaknesses inherent to legacy systems before they can deploy AI technologies at scale.
