RETAIL BANKING: THE BANK OF THE FUTURE
Artificial intelligence and advanced analytics are key to open banking
These accelerated innovations are essential for banks to move towards global transformation.
To foster continuous improvement beyond the first deployment, banks must augment homegrown AI models and talents
O
pen banking in markets such as Australia, Hong Kong, the Philippines, and Malaysia still has a kind of anxiety around it. However, with markets, such as Singapore, whose regulators show strong support as well as supportive players, it may as well be a matter of time, Irene Xu, director of banking practice for SAS Asia Pacific Global Industry Practice, observed. “In this region (Asia Pacific), we can expect open banking as the next biggest disruptive force in the financial services industry not only due to the fast rise of the extensive digital economies but also that we have observed very strong support from many local regulators in this region, with the intention to bring more competitive and better financial products and services to the community,” Xu said. Before the move to open 8 ASIAN BANKING AND FINANCE | Q4 2021
Open banking is the next biggest disruptive force in the financial services industry
banking, Xu stressed the importance of leveraging data and the use not only of artificial intelligence (AI) and machine learning (ML) but also advanced analytics (AA). McKinsey partners, Renny Thomas and Violet Chung, believe that lenders will need to move towards an enterprise-wide road map for deploying AA and ML models that would also include plans to embed AI in business processes. They stressed that to establish a robust AI-powered decision layer, banks will need to shift from attempting to develop specific use cases and point solutions to an enterprise-wide road map for deploying AA and ML models across entire business domains. “In addition to strong collaboration between business teams and analytics talent, this
requires robust tools for model development, efficient processes (e.g., for re-using code across projects), and diffusion of knowledge (e.g., repositories) across teams. Beyond the at-scale development of decision models across domains, the road map should also include plans to embed AI in business-as-usual processes. Often underestimated, this effort requires rewiring the business processes in which these AA/AI models will be embedded; making AI decisions ‘explainable’ to endusers, and a change-management plan that addresses employee mindset shifts and skills gaps,” they added. Thomas and Chung said that to foster continuous improvement beyond the first deployment, banks also need to establish infrastructure (e.g., data measurement) and processes (e.g., periodic reviews of performance, risk management of AI models) for feedback loops to flourish. Additionally, banks will need to augment homegrown AI models and talents. Four layers of AI banking To become AI-first, banks must invest in transforming capabilities across all four layers of the integrated capability stack: the engagement layer, the AIpowered decisioning layer, the core technology and data layer, and the operating model, Thomas and Chung explained. The first layer will entail reimagining customer engagement as customers expect banks to be always present in their end-user journeys as well as know their context and needs no matter where they interact with the bank, and to enable a frictionless experience. Second, building the AI-powered decision-making layer will enable delivering personalised messages and decisions to millions of users
