New technology from digital banking platform Plumery AI aims to address a dilemma for financial institutions: how to go beyond proof of concept and incorporate artificial intelligence into everyday banking without compromising governance, security, and regulatory compliance.
Plumery’s ‘AI Fabric’ is positioned by the company as a standardized framework for connecting generative AI tools and models to core banking data and services. According to Plumery, the product aims to reduce reliance on bespoke integrations and facilitate an event-driven, API-first architecture that can scale as an institution grows.
The challenges the company is trying to address are recognized in the sector. Banks have invested heavily in experimenting with AI over the past decade, but adoption remains limited in many cases. McKinsey research suggests that while generative AI has the potential to significantly improve productivity and customer experience in financial services, most banks struggle to move pilots into production due to fragmented data assets and existing operating models. The consultancy argues that enterprise-level AI deployments require shared infrastructure and governance, as well as reusable data products.
Plumerie founder and CEO Ben Goldin said in comments accompanying the product launch that financial institutions are clear about what they expect from AI.
“They want real operational use cases that improve customer experience and operations, but without compromising on governance, security and control,” he said. “Rather than adding another AI layer on top of a fragmented system, event-driven data mesh architectures transform the way banking data is generated, shared, and consumed.”
Fragmented data remains a barrier
Data fragmentation remains one of the obstacles to AI operations in banking. Many institutions rely on traditional core systems existing on new digital channels, creating silos in their products and customer journeys. Each AI initiative requires new integration efforts, security reviews, and governance approvals, increasing costs and slowing delivery.
Academic and industry research supports this diagnosis. Research on explainable AI in financial services points out that fragmented pipelines make it difficult to track decisions and increase regulatory risk, particularly in areas such as credit scoring and anti-money laundering. Regulators have made clear that banks must be able to explain and audit AI-driven results, regardless of where the model was developed.
Plumery says its AI Fabric addresses these issues by presenting domain-oriented banking data as a managed stream that can be reused across multiple use cases. The company claims that by separating systems of record from systems of engagement and intelligence, banks can innovate more securely.
Evidence that AI is already in production
Despite the challenges, AI is already embedded in many parts of the financial sector. Case studies compiled by industry analysts show the 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 the burden on call centers and improving response times. Other large banks are using predictive analytics to monitor loan portfolios and predict defaults. Santander has publicly explained that it uses machine learning models to assess credit risk and enhance portfolio management.
Fraud detection is also a mature field. Banks are increasingly relying on AI systems to analyze trading patterns and flag anomalous behavior more effectively than rule-based systems. The technology consultancy’s research notes that such models rely on high-quality data flows, and integration complexity remains a limiting factor for smaller institutions.
More sophisticated applications are emerging on the periphery. Academic research on large-scale language models suggests that under strict governance, conversational AI could support certain transactional and advisory functions in retail banking. However, these implementations are experimental and subject to close scrutiny due to regulatory implications.
Platform provider and ecosystem approach
Plumery operates in a competitive market for digital banking platforms, positioning itself as an orchestration layer rather than a core system replacement. The company has entered into partnerships designed to fit into the broader fintech ecosystem. Integration with Ozone API, an open banking infrastructure provider, was presented as a way for banks to deliver standards-compliant services faster without custom development.
Its approach reflects a broader industry trend toward composable architectures. Vendors like Backbase are pushing API-centric 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 for incremental innovation than large-scale system replacement.
Readiness remains uneven
Evidence suggests that preparation in this area is not uniform. A report by Boston Consulting Group found that less than a quarter of banks believe they are ready for large-scale AI implementation. The company argued that the gaps are in governance, data infrastructure, and operational discipline.
Regulators are responding by providing controlled environments for experiments. In the UK, regulatory sandbox initiatives have enabled banks to test new technologies, including AI. These programs aim to support innovation and strengthen accountability and risk management.
For vendors like Plumery, the opportunity lies in providing an infrastructure that reconciles technological ambitions with regulatory realities. AI Fabric enters a market where there is a clear demand for operational AI, where success will depend on proving new tools to be secure and transparent.
It remains unclear whether Plumery’s approach will be adopted as standard. As banks move from experimentation to production, the focus is shifting to architectures that support AI. In that context, platforms that can demonstrate technical flexibility and governance compliance are likely to play a key role in the next phase of digital banking.
(Image source: “Colorful Shale Formation 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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