Banking has emerged as one of the industries with the highest potential to benefit from generative AI. According to McKinsey, generative AI is expected to generate US$200 billion to US$340 billion in value for the banking industry, with its impact rippling across financial service institutions (FSIs) – from front to back office, corporate, and retail sectors.
This transformative technology will fundamentally reshape job functions and potentially spur new business models. However, for many banks, this vision remains an elusive utopia due to the prevalence of data silos and challenges in data movement. Until banks can address these issues, they will not be able to fully capitalise on the opportunities presented by generative AI.
Breaking down data silos to unlock AI’s potential in banking
A successful business is not just about possessing vast amounts of data; it’s about transforming and transmitting data in real time, delivering valuable insights, and enabling smarter operations.
Unfortunately, many banks are currently hampered by data silos, which occur when information is stored in isolated systems that do not communicate with each other. This creates a fragmented view of the customer or business as a whole, resulting in inconsistencies, inefficiencies, and a lack of comprehensive data sets. As long as data from various departments — such as customer service, risk management, and transaction processing — remains siloed, banks cannot fully leverage AI’s capabilities to analyse patterns, predict outcomes, and automate processes. Consequently, they miss out on the transformative benefits of generative AI to drive innovation and enhance operational efficiency.
To that end, FSIs need to look at the unified control of their data, which ensures consistency, accuracy, and efficiency in data handling across all departments. Beyond that, they also need to ensure that this data is streamed and routed in real time for actionable and timely insights. After all, having an AI act on outdated information will result in the AI-generated output being untimely or irrelevant, requiring more human interference to rectify.
To initiate the evolution of banking systems towards unified control, banks need to adopt a more event-driven approach to their data integration strategies. This real-time integration and distribution approach ensures smooth data flow across the organisation, reducing latency and enhancing operational efficiencies.
Enhancing AI capabilities in FSIs through event-driven integration
Traditionally, banks have operated using a request-driven model where a rigid architecture defines tasks. These systems efficiently handle simple and predefined tasks but fail to react to the variable demands of the digital era.
In this AI era, the key lies in establishing a continuous flow of information across the entire business and extending to and from customers. Real-time, event-driven communications between systems and services across environments and geographies enable this continuous flow. FSIs must be able to absorb and manage bursts of activity while ensuring data reliability and uptime, as even a few minutes of downtime can be devastating, leading to significant loss of customer satisfaction and financial damage.
Particularly in FSIs, triggering AI analysis at the appropriate moment is crucial, as the timing of insights can significantly impact decision-making and operational outcomes. Real-time AI applications require the processing of large volumes of data at high speed. Event-driven architecture (EDA) is well-suited for this, as its asynchronous nature efficiently handles the rapid generation of events.
Furthermore, the real-time nature of EDA can help connect and integrate applications and devices in the FSI ecosystem. Once AI analyses are complete, the resulting data and required actions can be disseminated instantly to relevant systems and personnel. An event-driven integration platform also enables FSIs to observe, audit, and govern the flow of events from end to end, allowing them to be more selective about what invokes an AI model while complying with policies and regulations.
Thankfully, the market is waking up to this reality. A recent global study by IDC found that more than 30% of financial services industry respondents have already adopted several EDA use cases in their organisations. This prompt dissemination is crucial for maintaining the agility and responsiveness necessary in today’s fast-paced financial environment, where decisions must be made quickly and accurately.
Embracing the future of banking with AI and EDA
The intersection of generative AI and banking is not just a technological convergence; it’s a revolution that is reshaping customer experiences, enhancing employee productivity, and propelling the industry into an era of unprecedented innovation. However, to fully harness the transformative power of generative AI, banks must move beyond traditional data handling methods and adopt more dynamic and responsive architectures.
By embracing an event-driven approach supported by EDA, banks can ensure timely and accurate AI analyses, maintain data reliability, and quickly disseminate actionable insights. Fundamentally, the combination of an event mesh with AI paves the way for continuous innovation and a future-ready banking ecosystem.
In a world where change is constant, empowering businesses to thrive by harnessing the power of their information is paramount. The ability to transform data into actionable insights becomes the key to success in the modern business environment, with AI taking centre stage.
