Written by K R Venkatraman, Head Product Architecture, Infosys Finacle
2nd July 2024
Banks today are compelled to re-assess and reshape their operating models and IT systems to meet fast-evolving customer and industry demands, as well as regulatory changes. A foundational start to achieving this is recomposing their technology architecture – to constantly adapt and innovate – and stay ahead in a disruptive, digital-first landscape.
Recomposing architecture involves restructuring a bank’s underlying technology and organisational structure to adapt to the demands of the digital age. This goes beyond simply adopting new technologies; it involves a fundamental shift in how banks operate.
Moving away from centralised legacy technologies such as mainframe and siloed environments, banks are now reimagining their architectural constructs by embracing cloud-native, microservices-driven architecture and the power of modern tools and technologies to augment their stack.
They are adopting steady monitoring, feedback loops, and iterative development practices to ensure their architecture remains at the forefront of change. As they continue on this path, there are three key considerations for many banks in the industry:
1. Unleashing the power of data and AI for robust architecture
The financial services industry is perceived to be one of the biggest beneficiaries of artificial intelligence (AI), especially in risk, marketing, customer experience, and money movement. Generative AI, according to McKinsey, has the potential to deliver significant new value to banks to the tune of $200-340 billion.
Research reveals that the banking industry generates 2.5 quintillion bytes of data per day. However, data that does not adhere to specific quality standards of accuracy, relevance, and completeness negatively impacts the optimal performance of AI and the reliability of insights formed, and it can even translate into missed opportunities.
Banks are placing paramount importance on data cleanliness to prevent this alarming development, resulting in augmented investments in this realm. They must strategically hone their focus on establishing streamlined and efficient data pipelines to deliver real-time or near-real-time insights, laying the foundation for advanced data architecture, application programming interface (API) frameworks, and eventing architecture to facilitate seamless data flow and processing.
Banks must consider the convergence of online analytical processing (OLAP) and online transaction processing (OLTP) through efficient data pipelines and event-driven architectures as a strong priority. Synchronous and asynchronous data exchange patterns will continue to play a crucial role, as most embedded analytics and AI engines will consume such patterns.
Another critical focus whilst harnessing the powerful synergy of data and AI is the meticulous integration of AI within banks’ architectural frameworks across the most optimal integration touchpoints. Banks can integrate AI at their front end, ranging from intelligent product insights for informed sales strategies to AI-driven personalised product discovery for customers.
This symbiosis of robust data management and embedded AI strategies will play out profoundly in 2024 and beyond.
2. AI and security: A powerful synergy for data protection
The associated annual cost of cyber risks is estimated to exceed $200 billion globally, marking it a major international security concern. As the financial custodians of societies and governments, banks must pay heed. Recently, Piero Cipollone, executive board member, European Central Bank (ECB), has cautioned: “Our financial system is only as strong as its weakest link.” And rightly so.
As the sector evolves, open banking and the democratisation of data are set to become more widespread. Generative AI will also be intricately woven into the fabric of banking experiences and operations. These developments will intensify the threat landscape.
Simultaneously, cybercriminals are leveraging increasingly sophisticated tools and becoming more aggressive, underscoring the need for continuous upgrades in cyber resilience. Data-focused regulatory initiatives like DORA and GDPR and rapidly evolving AI-specific regulations such as regulating high-risk AI applications, including mandatory risk assessment, transparency, and human oversight, are already exerting pressure on banks to elevate their information security practices to bolster resilience.
In this race to close the gaps in cyber resilience, banks will do well to adopt AI for cutting-edge data protection. AI can be used to prevent and detect cyberattacks by identifying anomalies in user, system and network behaviours in real time. With more importance on information security as a framework, non-invasive controls can be seamlessly integrated across products to foster a unified security posture.
Banks can reshape their strategies to embrace a comprehensive approach that demands application-level governance. This will help banks transition towards a holistic security-by-design paradigm across APIs, applications, platforms, networks, and data architecture. By using machine learning to identify breach patterns, fortify their defences and ensure swift responses to emerging threats, banks can look to drive proactive threat detection through continuous monitoring.
3. Strangulation pattern: the architectural approach enabling streamlined, phased banking transformations
The complexity of banking modernisation, particularly the cost and resource intensiveness associated with a big-bang approach, is one of several reasons many banks may still be stuck with a legacy technology platform. With contemporary architectural techniques such as the “strangulation pattern”, banks can achieve the desired modernisation in a streamlined manner.
The strangulation pattern is a software migration strategy involving forming a new software layer, the “strangler”, around the legacy banking system. This strangler interacts with the core system’s data and functionality through well-defined APIs. Gradually, new functionalities are developed within the strangler layer in parallel with the legacy systems, allowing the bank to independently test and refine the new functionalities. Over time, more and more functionalities, based on needs and complexity, are migrated from the core system to the strangler layer. As a result, the core system becomes less and less critical and can be retired entirely or kept as a backup system.
Not only does this approach minimise risk compared to a big-bank switchover, but it also allows business operations to continue with minimal disruption. To make this a successful framework, banks must take the co-existence path and incorporate sound architectural design principles, such as robust API design data synchronisation, in their approach. Only then will they be able to simplify their transformation journey with a modern and efficient technology infrastructure.
The road ahead for European banks
In an increasingly digital marketplace, European banks must accelerate their journey to recompose architecture, especially as AI and generative AI scale as strategic differentiators. The valuable symbiosis between data and AI must be harnessed for a range of retail and corporate banking services.
AI can also play a powerful role in bolstering banks’ security posture in the face of a growing threat landscape.
Lastly, banks can embark on their transformation journey with a modern and efficient technology infrastructure, giving them a much-needed digital boost.
Banks will discover that incorporating emerging technologies into their recomposability journey is a sure-fire success factor to drive business value, innovation, and customer satisfaction.
About the author:
K R Venkatraman (KRV) brings over 28 years of global technology experience in international banking and technology transformations. His experience spans multiple core banking vendors, start-up ecosystem and experience with clients through their digital transformation journey across multiple markets globally.
In his current role, KRV is the chief technical architect of the Finacle suite of products. He is responsible for defining and executing the technology strategy for the products. He provides technical leadership to our software development teams.
He has also been leading the technology transformation journey of Finacle into a cloud native product. He has been working along with Finacle architecture and product engineering teams in introducing contemporary architecture into our products in a pragmatic manner.
Source : fintechfutures.com
