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Artificial intelligence is no longer a futuristic concept in financial services — it is the present.
AI core banking modernisation
From fraud detection, customer service automation to core banking, AI is reshaping nearly every aspect of banking.
But beyond front-end enhancements, AI is also playing a transformative role in rearchitecting the very backbone of the industry: the core platforms on which banks and payment firms operate.
For decades, the sector has depended on legacy systems — monolithic, often fragmented platforms built on outdated programming languages and ill-suited to the agile demands of modern finance.
A Serious Risk
These systems, while once robust, now pose a serious risk.
They are expensive to maintain, slow to adapt, and increasingly exposed to cyber vulnerabilities.
As the pace of digital change accelerates, these platforms are beginning to buckle under the weight of their own limitations.
Leading institutions are taking decisive action.
JPMorgan Chase, under CEO Jamie Dimon’s leadership, has committed over £12 billion annually to data and technology innovation, deploying AI across more than 400 use cases.
This reflects a broader industry shift toward intelligent, data-centric infrastructure — a trend that is only gaining momentum.
A Critical Barrier
A critical barrier to this evolution is technical debt: the accumulated burden of past decisions to prioritise speed over scalability.
For many institutions, this debt manifests in patchwork systems cobbled together through years of mergers and integrations — a “Frankenstein” architecture that resists innovation.
Instead of ripping and replacing, many are now opting for cloud-native, microservice-based solutions that are modular, scalable, and better equipped to support continuous innovation.
AI Plays a Dual Role
This is where AI plays a dual role: as both catalyst and enabler.
AI requires clean, high-quality data to deliver actionable insights.
Yet most banks struggle with fragmented data environments plagued by silos and inconsistencies.
A data-first strategy, anchored by a single source of truth (SSOT), is essential.
Centralising data not only enhances AI’s efficacy but also unlocks a new level of operational intelligence — allowing firms to track customer behaviour, streamline processes, and identify inefficiencies with surgical precision.
Firms such as Bank of America and Wells Fargo exemplify what is possible.
Bank of America’s AI assistant, Erica, now serves millions of users with personalised financial insights, while Wells Fargo’s revamped data infrastructure has bolstered risk management capabilities.
These are not IT projects — they are core business enablers, driven by top-down leadership.
But transitioning from legacy to modern architecture is not without risk.
The so-called “strangler pattern” offers a pragmatic path forward.
Instead of attempting a high-risk wholesale replacement, institutions can incrementally reengineer legacy systems — function by function — while maintaining business continuity.
New components run in parallel, allowing for refinement and rollback if necessary. This reduces exposure while accelerating the shift to agile, outcome-focused systems.
Ultimately, the modernisation of core platforms is not just a technical imperative — it’s a strategic one.
Institutions that hesitate may find themselves overtaken by more nimble competitors. In a digital economy where trust, speed, and experience define success, banks must embrace AI not merely as a tool, but as a foundation.
Financial services firms stand at a pivotal juncture. Modernise or risk irrelevance? The era of data-driven, AI-powered core platforms has arrived.
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