By Phil Scanlon, Senior Vice President for Global Solution Engineering, Solace
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 – implying 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, they need to evolve their digital interactions from simply providing ‘services’ into generating ‘conversations’. This is crucial for fostering deeper connections with customers, allowing retail banks to move beyond transactional exchanges to engaging dialogues that resonate on a personal level.
Artificial Intelligence (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 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. 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.
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. That said, 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 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 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 powers real-time business operations. By tapping into this rich and timely data source, retail banks can swiftly integrate AI into their existing business processes. The context mesh also allows new business contexts to be easily integrated and published to the mesh, thereby expediting digital transformation efforts and enabling faster, more efficient AI adoption.
Greater Innovation, Enhanced Customer Experiences
A context mesh enables retail banks to quickly and cost-effectively develop and deploy AI-driven products and services. For instance, a retail bank could use the mesh to feed an AI-powered virtual assistant with real-time customer profiles, preferences, and market trends – creating a more sophisticated assistant that delivers tailored financial recommendations.
Furthermore, this access to real-time data through the context mesh allows retail banks to continuously develop and refine the service, which not only improves the customer experience but also drives operational efficiency through automated financial planning and market analysis.
Future-proof AI Initiatives
The flexible and scalable nature of a context mesh allows retail banks to seamlessly trial and deploy new AI models without significant system overhauls. This adaptability ensures that retail banks can keep pace with evolving business needs and industry trends, while maintaining a strong foundation for AI innovation.
Event-driven integration as the way forward for customer service
AI presents great capabilities to strengthen customer relationships, bringing retail banks closer to the goal of becoming ‘life-centric’. Retail banks cannot afford to look at AI as just a technological upgrade, but rather a much-needed shift towards putting the customer at the centre of the loyalty experience.
However, the right event-driven integration strategy has to be in place for retail banks to fully capitalise on the benefits of AI, and a context mesh is well-positioned to provide unified, real-time contextualised insights for retail banks to gain a competitive edge for customer loyalty.
