Banks operationalise as Plumery AI launches standardised integration

A new technology from digital banking platform Plumery AI aims to address a dilemma for financial institutions: how to move beyond proofs of concept and embed artificial intelligence into everyday banking operations without compromising governance, security, or regulatory compliance.

Plumery’s “AI Fabric” has been positioned by the company as a standardised framework for connecting generative AI tools and models to core banking data and services. According to Plumery, the product is intended to reduce reliance on bespoke integrations and to promote an event-driven, API-first architecture that can scale as institutions grow.

The challenge it seeks to address is recognised in the sector. Banks have invested heavily in AI experimentation over the past decade, but many deployments remain limited. Research by McKinsey suggests that while generative AI could materially improve productivity and customer experience in financial services, most banks struggle to translate pilots into production because of fragmented data estates and incumbent operating models. The consultancy argues that enterprise-level AI adoption requires shared infrastructure and governance, and reusable data products.

In comments accompanying the product launch, Plumery’s founder and chief executive, Ben Goldin, said financial institutions are clear about what they expect from AI.

“They want real production use cases that improve customer experience and operations, but they will not compromise on governance, security or control,” he said. “The event-driven data mesh architecture transforms how banking data is produced, shared, and consumed, not adding another AI layer on top of fragmented systems.”

Fragmented data remains a barrier

Data fragmentation remains one of the obstacles to operational AI in banking. Many institutions rely on legacy core systems that sit in newer digital channels, creating silos in products and customer journeys. Each AI initiative requires fresh integration work, security reviews, and governance approvals, thus increasing costs and slowing delivery.

Academic and industry research supports this diagnosis. Studies on explainable AI in financial services note that fragmented pipelines make it harder to trace decisions and increase regulatory risk, particularly in areas like credit scoring and anti-money-laundering. Regulators have made clear that banks must be able to explain and audit AI-driven outcomes, regardless of where the models are developed.

Plumery says its AI Fabric addresses such issues by presenting domain-oriented banking data as governed streams that can be reused in multiple use cases. The company argues that separating systems of record from systems of engagement and intelligence allows banks to innovate more safely.

Evidence of AI already in production

Despite the challenges, AI is already embedded in many parts of the financial sector. Case studies compiled by industry analysts show widespread use of machine learning and natural language processing in customer service, risk management, and compliance.

Citibank, for example, has deployed AI-powered chatbots to handle routine customer enquiries, reducing pressure on call centres and improving response times. Other large banks use predictive analytics to monitor loan portfolios and anticipate defaults. Santander has publicly described its use of machine learning models to assess credit risk and strengthen portfolio management.

Fraud detection is another mature area. Banks rely increasingly on AI systems to analyse transaction patterns, flagging anomalous behaviour more effectively than rule-based systems. Research from technology consultancies notes that such models depend on high-quality data flows, and that integration complexity remains a limiting factor for smaller institutions.

More advanced applications are emerging at the margins. Academic research into large language models suggests that, under strict governance, conversational AI could support certain transactional and advisory functions in retail banking. However, these implementations remain experimental and are closely scrutinised due to their regulatory implications.

Platform providers and ecosystem approaches

Plumery operates in a competitive market of digital banking platforms that position themselves as orchestration layers rather than replacements for core systems. The company has entered partnerships designed to fit into broader fintech ecosystems. Its integration with Ozone API, an open banking infrastructure provider, was presented as a way for banks to deliver standards-compliant services more quickly, without custom development.

Its approach reflects a wider industry trend towards composable architectures. Vendors like Backbase and others promote API-centric platforms that allow banks to plug in AI, analytics, and third-party services to the existing core. Analysts agree generally that such architectures are better suited to incremental innovation than large-scale system replacement.

Readiness remains uneven

Evidence suggests that readiness in the sector is uneven. A report by Boston Consulting Group found that fewer than a quarter of banks believe they are prepared for large-scale AI adoption. The gap, it argued, lies in governance, data foundations, and operating discipline.

Regulators have responded by offering controlled environments for experimentation. In the UK, regulatory sandbox initiatives allow banks to test new technologies, including AI. These programmes are intended to support innovation and reinforce accountability and risk management.

For vendors like Plumery, the opportunity lies in providing infrastructure that aligns technological ambition and regulatory reality. AI Fabric enters a market where demand for operational AI is apparent, but where success depends on proving that new tools can be safe and transparent.

Whether Plumery’s approach becomes a adopted standard remains uncertain. As banks move from experimentation to production, the focus is moving towards the architectures that support AI. In that context, platforms that can demonstrate technical flexibility and governance adherence are more likely to play an important role in the digital banking’s next phase.

(Image source: “Colorful Shale Strata of the Morrison Formation at the Edge of the San Rafael Swell” by Jesse Varner is licensed under CC BY-NC-SA 2.0.)

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