Digital transformation has undeniably enhanced retail banks’ ability to meet customer needs and make their services more accessible than ever. Almost everyone today can access their bank accounts at their fingertips. However, shifting their customer engagement from physical to digital has come at a cost: The banking experience has increasingly become emotionally void. As banking transactions and interactions move online, the personal touch that has once defined customer engagement is waning, leaving consumers feeling like just another number in the system.
The impact of this change is significant. Many retail banks are losing the personal connection that fosters loyalty among their customers. Close to half (42%) of consumers now find it challenging to distinguish between financial services brands, according to Accenture’s study “Banking on AI – Banking Top 10 Trends for 2024.” This implies a growing homogeneity in the sector and an uphill battle to optimise customer experiences and build brand loyalty.
AI could be the key to moving away from product-centric to experience-centric
To fully harness the potential of digitalisation, retail banks need to evolve their digital interactions from simply providing ‘services’ to generating ‘conversations’. This is crucial for fostering deeper connections with customers, allowing banks to move beyond transactional exchanges to engaging dialogues that resonate on a personal level.
AI plays a pivotal role in this transformation. By leveraging AI technologies, retail banks are gradually personalising customer experiences at scale. Chatbots, for example, are invaluable for addressing generic customer inquiries and, more importantly, help retail banks provide tailored and useful financial advice that meets individual needs by analysing vast amounts of customer data.
Leveraging AI-driven solutions to provide round-the-clock support for customer issues and deliver timely, relevant financial solutions is one way for retail banks to enhance customer satisfaction, engagement, and most important of all, loyalty. That being said, the potential of AI is often hindered by the challenges posed by legacy banking systems.
Closing the AI gap
These legacy systems create significant data silos, making it challenging to integrate vast amounts of information from various departments, such as loyalty programs and transaction records. AI cannot develop a comprehensive understanding of individual customers without a unified view of customer data, limiting its ability to generate real-time insights and effectively offer personalised experiences.
Moreover, AI scalability is often a concern with legacy banking systems. AI implementation requires constant experimentation and exploration to discover effective solutions. However, even innovations that appear promising in theory may face challenges in scaling effectively for practical use. As a result, these solutions may struggle to be production-ready, hindering AI’s ability to serve customers across multiple channels.
Powering AI capabilities with event-driven integration
Integrating AI successfully into retail banking services will require real-time situational context, effective scalability and seamless data transmission across diverse environments. However, integration technology alone is not enough to fully take advantage of what AI has to offer.
What is required here is a data distribution layer that not only supports connectivity and integration, but also ensures the real-time distribution of immense volumes of data.
Enter the context mesh
This data distribution layer is known as a context mesh, which is an application of an event mesh – an interconnected network of event brokers that routes real-time information (think data as events) between applications and devices globally. For example, interactions like a customer tapping a payment card, or engaging with a robo-advisor, generate events that are transmitted through this mesh.
The transformation of an event mesh into a context mesh occurs when AI agents are integrated and fed with real-time information from the event mesh. In essence, the context mesh – true to its name – aggregates context from various systems to form a foundation for AI-driven applications.
Furthermore, as a context mesh is underpinned by event-driven integration, organisations can quickly unlock events from existing applications. Central to this integration is the event broker, which enables the transmission of events between different system components, acting as a mediator between publishers and subscribers. An event broker is the cornerstone of event-driven architecture, and all event-driven applications use some form of an event broker to transmit and receive data.
Achieving better banking experiences with a context mesh
By becoming more event-driven and leveraging a context mesh, retail banks can stand to benefit from:
- Accelerated AI adoption
Event-driven integration supports real-time business operations. By using this timely data source, retail banks can integrate AI into their existing business processes more effectively. The context mesh also enables new business contexts to be incorporated into the mesh, potentially facilitating digital transformation efforts and speeding up AI adoption. - Greater innovation, refined customer experiences
A context mesh can help retail banks develop and deploy AI-driven products and services more efficiently. For instance, a retail bank might use the mesh to supply an AI-powered virtual assistant with customer profiles, preferences, and market data. This could enable the assistant to provide financial recommendations tailored to customer needs.
Access to real-time data through the context mesh may also allow banks to refine services continuously. This can contribute to improving the customer experience and operational processes, such as financial planning and market analysis. - Adaptable AI initiatives
The flexible nature of a context mesh allows retail banks to trial and deploy new AI models with fewer system disruptions. This adaptability could help banks adjust to changing business needs and industry trends while maintaining a functional framework for AI innovation.
Event-driven integration as a pathway for customer service
AI has the potential to improve customer relationships by enabling retail banks to focus more on customer needs. To achieve this, banks need to view AI not as a simple technological upgrade, but as an opportunity to place the customer at the centre of their strategy for loyalty.
An effective event-driven integration strategy is critical for fully realising AI’s benefits. A context mesh can help unify and contextualise real-time data, offering retail banks insights to enhance their customer loyalty efforts.
