Banks operationalise as Plumery AI launches standardised integration

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.

Importance for Students

This development is crucial for students pursuing a career in finance and banking, as it highlights the growing importance of AI in the sector and the need for standardised frameworks to integrate AI tools and models into core banking operations.