Financial services organizations will live and die by audience connection. In a world where one's customers (and even prospective customers) double as brand ambassadors, digging into the details and understanding their mindset is essential. With a wide-open playing field unencumbered by geographical boundaries, market entrants cannot afford to be niche-averse. 

In the dynamic realm of customer-centric business models, organizations are increasingly adopting advanced technologies and strategies to augment the capabilities of Customer Relationship Management (CRM) systems. 

Key enablers of hyper-personalization are artificial intelligence (AI) and machine learning (ML), which play a pivotal role in this evolution. These technologies empower CRM systems to analyze extensive datasets, extracting valuable insights into individual customer behaviours, preferences, and needs. Incorporating predictive analytics and recommendation engines allows real-time customization of interactions, ensuring customers receive personalized content, product recommendations, and communication channels tailored to their unique profiles. 

GenAI in the financial sector

Competitive pressures have fueled the rapid adoption of AI/ML in the financial sector in recent years by facilitating gains in efficiency and cost savings, reshaping client interfaces, enhancing forecasting accuracy, and improving risk management and compliance. GenAI could also deliver cybersecurity benefits, from implementing predictive models for faster threat detection to improved incident response. 

Financial service providers have quickly explored the capabilities of GenAI and how it can be adapted to a broad range of applications (Box 1). GenAI's ability to process vast and diverse data sets and generate content in inaccessible and easily usable formats (including conversational) is proving very useful in enhancing efficiency and improving financial providers' customer experience, risk mitigation, and compliance reporting. However, the deployment of GenAI in the financial sector has risks that need to be fully understood and mitigated by the industry and prudential oversight authorities.

Hyper-personalization and GenAI

A hyper-personalization strategy has four elements: data foundation, decisions, design, and distribution. All these elements are necessary; however, data foundation is the starting point because a hyper-personalization strategy needs a customer's feedback to deliver the experiences. For example, if all customers are unknown, a hyper-personalization approach will help build the customer database to receive hyper-personalized experiences later on.

In a statement, Michael Haney, head of the product strategy at Galileo Financial Technologies, remarked that AI could transform financial services across several workflows and interactions, among them client servicing — enhancing and improving the productivity of operations. 

GenAI-powered technologies like virtual assistants provide value for both customers and bank staff. For instance, instead of having to peruse manuals that are hundreds of pages thick, staffers can type a question into their AI-powered technologies, such as virtual assistants, to provide value for both customers and bank staff. 

Generative AI can also improve loan decisions and other interactions supporting loan lifecycle management, from applications to credit collections. AI is also an aid to treasury managers within various banks to examine cash flow and interest rate changes and navigate liquidity risk.

In Haney's opinion, hyper-personalization will be a natural by-product of AI. Consumers have traditionally had to manually navigate through many payment options, spanning everything from ACH to wires and most recently, real-time options. GenAI acts as an "engine" to help quickly guide them through the options.