Deploying AI capabilities across the organisation requires a scalable, resilient, and adaptable set of core-technology components. When implemented successfully, this foundational layer can enable a bank to accelerate technology innovations, improve the quality and reliability of operations, reduce operating costs, and strengthen customer engagement.
An AI-first model places demands on a bank’s core technology
Financial institutions that have shifted from being intensive consumers of technology to making AI and analytics a core capability are finding it easier to shift into the real-time and consumer-centric ecosystem.
As AI technologies play an increasingly central role in creating value for banks and their customers, financial-services organisations need to reinvent themselves as technology-forward institutions, so they can deliver customised products and highly personalised services at scale in near real time.
At many institutions, standard practices now include omni channel engagement, the use of APIs to support increased real-time information exchange across systems, and the use of big data analytics to improve credit underwriting, evaluate product usage, and prioritise opportunities for deepening relationships.
As financial-services organisations continue to mature, the increasing demands on the technology infrastructure to support more complex use cases involving analytics and real-time insights are pushing firms to re-examine their overall technology function.
Once they have committed to modernising the core technology and data infrastructure underpinning the engagement and decision-making layers of the capability stack, banks should organise their transformation around six crucial demands: technology strategy, superior experiences, scalable data and analytics platforms, scalable hybrid infrastructure, configurable product processors, and cybersecurity strategy.
Robust strategy for building technology capabilities
Banks should develop a road map for transformation that focuses on three dimensions of value creation: faster time to market with efficient governance and productivity tracking, clear alignment of demand and capacity to meet strategic and near-term priorities, and a well-defined mechanism to coordinate “change the bank” and “run the bank” initiatives according to their potential to generate value.
Faster time to market requires efficient and repeatable development and testing practices, coupled with robust platforms and productivity-measurement tools. Aligning demand and capacity according to strategic priorities works on two levels.
On one level, banks need to ensure that execution, infrastructure, and support capacity are optimised to ensure constant operation of all use cases and journeys. In addition, with constant uptime assured, work should be organised and scheduled to expedite projects having the greatest impact on value.
Finally, financial institutions should establish clear mechanisms for setting priorities and ensuring that each use case is designed and built to generate a return exceeding capital investments and operating costs.
Technology leaders should prioritise interconnected capabilities
Given the broad scope of components to be transformed, organisations should bear in mind that optimal outcomes are much likelier when they first establish a holistic strategy for technology transformation. Unfortunately, not all have found the resources to embrace fully the potential offered by the rapid advancement of AI technologies and the steady rise in customer expectations.
Some financial institutions, despite seeing the imperative to change, have maintained and modernised their legacy platforms. Various business lines have set up organically built platforms upon this foundation, making it costlier and more and more complex to maintain.
Many organisations have spent billions of dollars on multiyear technology initiatives within silos, only to find that they fail to generate the scale benefits required to justify investments. Leaders should heed these lessons, adopt a holistic perspective, and map priorities according to the end-to-end impact that each step in the technology transformation has on the value of the enterprise.
Technology transformations are fraught with risk, including delays and cost overruns, and only those organisations whose leaders are prepared to commit the energy and capital necessary to carry through with the comprehensive effort should embark on the journey.
Ultimately, this is a decision not just to survive, but to thrive, and it requires a change in mindset. Specifically, traditional financial institutions will need to break out of their legacy technology architecture and explore AI-and-analytics opportunities.
If banks are to thrive in a world where customer expectations are increasingly shaped by the AI-and-analytics capabilities of technology leaders, they must rebuild their core technology and data infrastructure to support AI-powered decision making and reimagined customer engagement.
These are the three “technology layers” of the AI-bank capability stack. The full stack also includes a leading-edge operating model to ensure that all layers work together in unison to deliver intelligent propositions through smart servicing and experiences. The AI bank of the future requires an agile culture and platform-oriented operating model that respond promptly to emerging opportunities and deliver innovative solutions rapidly at scale.
The full report, Beyond digital transformations: Modernizing core technology for the AI bank of the future, deep dives into the considerations and key transformation required when modernising an organisation’s core technology, as well as 12 actions that banking leaders should consider taking to ensure the transformation creates value for customers and the bank.
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