Plumery AI Unveils “AI Fabric” to Bridge the Gap Between AI Experimentation and Production in Banking
Financial institutions face a persistent challenge: how to integrate artificial intelligence into their daily operations without jeopardizing governance, security, or regulatory compliance. Plumery AI, a digital banking platform provider, is introducing its “AI Fabric” technology, aiming to solve this very dilemma.
The company describes AI Fabric as a standardized framework designed to connect generative AI tools and models with core banking data and services. This approach aims to reduce the need for custom integrations and promote an event-driven, API-first architecture that can scale with institutional growth.
This challenge is widely acknowledged within the financial sector. Banks have significantly increased their investment in AI over the past decade, yet many deployments remain in pilot phases. Research from McKinsey indicates that while generative AI holds substantial potential to boost productivity and enhance customer experience in financial services, most banks struggle to transition these experiments into full production. This difficulty is often attributed to fragmented data landscapes and entrenched operating models. McKinsey emphasizes that enterprise-level AI adoption necessitates shared infrastructure, robust governance, and reusable data products.
In a statement accompanying the product launch, Plumery’s founder and CEO, Ben Goldin, highlighted the clear expectations financial institutions have for AI. “They are looking for practical, production-ready use cases that genuinely improve customer experience and operational efficiency, but they will not compromise on governance, security, or control,” Goldin stated. He added, “Our event-driven data mesh architecture redefines how banking data is produced, shared, and consumed, rather than simply adding another AI layer on top of existing, fragmented systems.”
**Persistent Data Fragmentation Hinders AI Adoption**
Data fragmentation continues to be a significant impediment to widespread AI implementation in banking. Many institutions operate with legacy core systems that are overlaid with newer digital channels, creating silos across products and customer interactions. Each new AI initiative requires dedicated integration work, security assessments, and governance approvals, inevitably increasing costs and delaying deployment timelines.
This diagnostic is supported by academic and industry research. Studies focusing on explainable AI in financial services highlight that fragmented data pipelines make it more difficult to track decision-making processes, thereby increasing regulatory risk, particularly in critical areas like credit scoring and anti-money-laundering. Regulators have made it clear that banks must be able to explain and audit AI-driven outcomes, irrespective of where the underlying models were developed.
Plumery asserts that its AI Fabric addresses these issues by presenting domain-specific banking data as governed data streams that can be readily repurposed across multiple use cases. The company’s philosophy is that by separating systems of record from systems of engagement and intelligence, banks can innovate with greater safety and confidence.
**Evidence of AI in Production Across the Financial Sector**
Despite the ongoing challenges, AI is already integrated into numerous facets of the financial services industry. Case studies compiled by industry analysts reveal extensive use of machine learning and natural language processing in customer service, risk management, and compliance functions.
For instance, Citibank has implemented AI-powered chatbots to manage routine customer inquiries, thereby alleviating pressure on call centers and improving response times. Other major banks leverage predictive analytics to monitor loan portfolios and proactively identify potential defaults. Santander has publicly shared its experience using machine learning models for credit risk assessment and enhancing portfolio management.
Fraud detection represents another mature application area. Banks increasingly rely on AI systems to analyze transaction patterns, effectively flagging anomalous behavior in ways that traditional rule-based systems cannot. Technology consulting firms note that the success of these models is contingent on high-quality data flows, and that integration complexity often remains a bottleneck, especially for smaller institutions.
More sophisticated AI applications are emerging on the periphery. Academic research into large language models suggests that, under stringent governance frameworks, conversational AI could support specific transactional and advisory functions within retail banking. However, these implementations are still largely experimental and are subject to close scrutiny due to their significant regulatory implications.
**The Role of Platform Providers and Ecosystem Approaches**
Plumery operates within a competitive landscape of digital banking platform providers that position themselves as orchestration layers rather than direct replacements for core banking systems. The company has established strategic partnerships to integrate into broader fintech ecosystems. Its integration with Ozone API, a provider of open banking infrastructure, was presented as a means for banks to accelerate the delivery of standards-compliant services without the need for custom development.
This approach aligns with a broader industry trend towards composable architectures. Vendors such as Backbase and others are promoting API-centric platforms that enable banks to seamlessly integrate AI, advanced analytics, and third-party services into their existing core systems. Industry analysts generally concur that such architectures are more conducive to incremental innovation than large-scale system overhauls.
**Uneven Readiness Across the Sector**
Evidence suggests a disparity in readiness across the financial sector. A report by Boston Consulting Group found that fewer than a quarter of banks believe they are adequately prepared for large-scale AI adoption. The report identified gaps in governance, foundational data infrastructure, and operational discipline as key areas requiring attention.
In response, regulators are actively creating controlled environments for experimentation. Initiatives like regulatory sandboxes in the UK allow banks to test new technologies, including AI, in a secure setting. These programs are designed to foster innovation while simultaneously reinforcing accountability and risk management practices.
For technology vendors like Plumery, the opportunity lies in developing and providing infrastructure that harmonizes technological ambition with regulatory realities. AI Fabric enters a market where the demand for operational AI is clear, but where success hinges on demonstrating that new tools can be implemented safely and transparently.
While it remains uncertain whether Plumery’s approach will become an industry standard, its entry signals a critical shift. As banks move from AI experimentation to production deployment, the focus is increasingly turning towards the underlying architectures that support these advanced technologies. In this evolving landscape, platforms that can showcase both technical flexibility and steadfast adherence to governance principles are well-positioned to play a pivotal role in the next phase of digital banking transformation.
Original article, Author: Samuel Thompson. If you wish to reprint this article, please indicate the source:https://aicnbc.com/15839.html
