How real-time contextual data can drive innovation and CX in retail banking

Integrating contextually-aware data distribution layers in event-driven architecture for AI processing can help banks boost customer experience/satisfaction and loyalty

Today, almost anyone can access their bank accounts digitally. However, shifting customer engagement from physical to digital has come at a cost: the banking experience has increasingly become emotionally void.

The personal touch that once defined customer experience (CX) is waning, leaving consumers feeling like just another number in the system. 

To fully harness the potential of digitalization, banks need to evolve their digital interactions from simply providing services into generating conversations: moving beyond transactional exchanges to engaging dialogs that resonate on a personal level. 

AI could be the key

By leveraging AI technologies, retail banks are gradually personalizing customer experiences at scale, by using increasingly sophisticated chatbots, leveraging round-the-clock AI-powered support for customer issues, and delivering timely, relevant solutions to enhance customer satisfaction, engagement and loyalty.

That said, the potential of AI is often hindered by the challenges posed by legacy banking systems. These legacy systems create significant data silos, making it challenging to integrate vast amounts of information from various departments.

AI cannot develop a comprehensive understanding of individual customers without a unified view of customer data, and its ability to generate real-time insights and effectively offer personalized experiences will be curtailed. Moreover, AI scalability is often a concern with legacy banking systems. The implementation of AI 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. 

Event-driven integration in AI 

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 is a data distribution layer that not only supports connectivity and integration, but also ensures the real-time distribution of immense volumes of data. 

In event-driven architecture (EDA), this data distribution layer can be called a “context mesh” (generically a “contextual data distribution layer”): an interconnected network of event brokers that route real-time information (think data as events) between applications and devices globally. For example, interactions such as a customer tapping a payment card, are transmitted through this context mesh.

The transformation of a traditional even-driven integration layer in to a context-aware data distribution layer (what Solace calls “the context mesh”) occurs when AI agents are integrated and fed with real-time information from the event mesh. In essence, the context mesh aggregates context from various systems to form a foundation for AI-driven applications.

Furthermore, as a context mesh is underpinned by event-driven integration, organizations can quickly unlock events from existing applications. Central to this integration is the event broker, which facilitates smart and reliable transmission of events between different system components, acting as a mediator between publishers and subscribers.

An event broker is the cornerstone of EDA, and all event-driven applications use some form of an event broker to transmit and receive data. 

Enhancing CX with better context