Banks operationalise as Plumery AI launches standardised integration

New technology from digital banking platform Plumery AI aims to address financial institutions’ dilemma: how to go beyond proofs of concept and integrate AI into everyday banking operations without compromising governance, security or regulatory compliance.

The company has developed Plumery’s “AI Fabric” as a unified framework for connecting generative AI tools and models to core banking data and services. According to Plumeri, the product aims to reduce reliance on custom integrations and promote an event-driven API architecture that can scale as organizations grow.

The challenge it seeks to address is recognized within the sector. Banks have invested heavily in AI trials over the past decade, but many deployments remain limited. Research from McKinsey suggests that while generative AI can measurably improve productivity and customer experience in financial services, most banks struggle to translate pilots into production due to fragmented data sets and current operating models. The consulting firm says enterprise-level adoption of AI requires shared infrastructure, governance, and reusable data products.

In comments accompanying the product launch, Ben Goldin, founder and CEO of Bloomery, said financial institutions are clear about what they expect from AI.

“They want real productivity use cases that improve customer experience and operations, but they won’t give up governance, security or control,” he said. “An event-driven data mesh architecture transforms how banking data is produced, shared and consumed, without adding another AI layer on top of fragmented systems.”

Fragmented data remains a bottleneck

Data fragmentation remains one of the barriers to operationalizing AI in banking. Many organizations rely on legacy platforms found in newer digital channels, creating silos in product and customer journeys. Each AI initiative requires new integration work, security reviews, and management approvals, increasing costs and slowing delivery.

Academic and industry research supports this diagnosis. Studies on explainable AI in financial services suggest that fragmented pipelines make it difficult to track decisions and increase regulatory risks, especially in areas such as credit scoring and anti-money laundering. Regulators have made clear that banks must be able to explain and audit the results that AI is based on, regardless of where the models are developed.

Plumery says its AI Fabric addresses such issues by presenting domain-oriented banking data as controlled flows that can be reused across multiple use cases. The company says that separating scoring systems from engagement and intelligence systems allows banks to innovate more securely.

Proof of AI is already in production

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

For example, Citibank deployed AI-powered chatbots to handle routine customer inquiries, reducing pressure on call centers 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 enhance portfolio management.

Fraud detection is another mature area. Banks are increasingly relying on artificial intelligence systems to analyze transaction patterns and report anomalous behavior more effectively than rule-based systems. Research by technology consulting firms suggests that such models rely on high-quality data flows, and that integration complexity remains a limiting factor for smaller organizations.

More advanced applications appear in the margins. Academic research into large language models suggests that, under strict management, conversational AI can support some transactional and advisory functions in retail banking. However, these applications remain experimental and are subject to close scrutiny due to their regulatory implications.

Platform providers and ecosystem approach

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

Its approach reflects a broader industry trend toward composable architectures. Vendors like Backbase and others are promoting API-focused platforms that allow banks to plug AI, analytics, and third-party services into their existing core. Analysts generally agree that such architectures are better suited to incremental innovation than large-scale system replacement.

Preparedness remains uneven

Evidence suggests that preparedness in this sector is mixed. A report from Boston Consulting Group found that less than a quarter of banks believe they are ready to adopt AI on a large scale. He claimed that the gap lies in governance, data foundations and operational discipline.

Regulatory bodies have responded by providing controlled environments for experimentation. In the UK, regulatory sandbox initiatives allow banks to test new technologies, including artificial intelligence. These programs aim to support innovation and enhance accountability and risk management.

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

Whether the Plumeri approach will become an adopted standard remains uncertain. As banks move from experimentation to production, the focus is shifting toward AI-enabled infrastructure. In this context, platforms that can demonstrate technical agility and commitment to governance are likely to play an important role in the next phase of digital banking.

(Image source: “Coloured shale layers of the Morrison Formation at the edge of the San Rafael Swell” by Jesse Varner licensed under CC BY-NC-SA 2.0.)

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