How the Revvence LLM Fabric™ Enables Enterprise-Grade AI Across Risk, Finance, and Sustainability.
Banks are navigating a critical phase in their digital evolution. Recent advances in generative AI—specifically, large language models (LLMs)—offer significant enhancements in capability. These models are no longer novel experiments; their ability to interpret language, generate coherent narratives, and apply reasoning to both textual and numerical data has transformed them into credible tools for change.
However, while many banks have made early forays into LLM-based tools, most deployments remain limited in scope, confined to productivity enhancements or narrow domain prototypes. Without a strategic architectural model to unify these efforts, the risk is that LLM adoption becomes fragmented, duplicative, and ultimately ineffectual.
A more coherent and enterprise-aligned approach is now emerging: we call it the Revvence LLM Fabric.
Disclosure: This blog was created based on a conversation (or series of prompts) with Revvy, a ChatGPT developed by Revvence and trained on finance transformation and systems change-related content.
The Revvence LLM Fabric™ is not a product or platform. It is an enterprise architectural pattern that positions LLMs as a governed, reusable, and integrated capability across the organisation. Rather than creating isolated pilots, the LLM Fabric introduces a common semantic and generative layer that supports internal reasoning, workflow augmentation, and language-based automation across multiple business units.
It is designed to be secure, grounded in the bank’s internal content and policies and processes, and built with governance at its core. The Revvence LLM Fabric brings together:
Executed correctly, this enables a distributed model of intelligence: an enterprise where LLMS can assist safely in processes that require expert judgement and narrative reasoning, such as regulatory commentary, stress scenario design, policy interpretation, cost and capital planning, financial forecasting, scenario modelling, financial close, and ESG classification.
The Revvence LLM Fabric is our proprietary approach to building this capability, drawing on our experience across regulated institutions and deep understanding of finance, risk, and sustainability domains.
Today, many banks are already exposed to generative AI through enterprise tooling:
These tools enhance individual productivity, reduce the friction of routine content creation, and improve human focus. However, they do not fundamentally change the way a bank operates.
The LLM Fabric addresses a different challenge: how to apply the full spectrum of modern AI capabilities — including generative AI, retrieval-augmented generation (RAG), agentic RAG, and agentic AI — to core enterprise processes in a secure, explainable manner embedded within the bank's operating model.
While generative models enable content creation and narrative reasoning, the true value arises when they are paired with the structured retrieval of internal data and documentation, further orchestrated as autonomous agents that can reason, plan, and act within specific banking workflows.
This allows the LLM Fabric to assist with orchestration, validation, and assurance of complex planning and regulatory processes. Rather than duplicating AI features in Oracle EPM or ERP, the Fabric promotes cross-functional alignment, context-aware decision support, and governance in activities like regulatory interpretation, scenario validation, policy traceability, and disclosure consistency, especially where outputs span multiple systems, owners, and timeframes.
The LLM Fabric enables systemic augmentation of knowledge-intensive workflows across finance, risk, treasury, and sustainability. Rather than generating content from scratch, it supports decision-making by retrieving historical reasoning, comparing proposed outputs to internal norms, and identifying inconsistencies in regulatory narrative or scenario logic.
In planning, for example, the Fabric can retrieve how previous capital plans responded to specific macroeconomic trends, helping users align new narratives with past assumptions. In treasury, it can surface past regulatory expectations for liquidity buffers under stress and flag deviations from policy. In risk, it enables model validation teams to compare scenario logic against prior ICAAP submissions and governance-approved frameworks.
These capabilities do not replace expert judgment but provide analysts and SMES with a reasoning co-pilot, enabling more consistent, auditable, and aligned decision support across the enterprise.
As an Oracle partner, Revvence references Oracle Cloud Infrastructure and business application technologies throughout this solution because we believe Oracle currently offers the most complete and integrated platform for deploying LLMs in enterprise financial services contexts.
However, the architectural pattern we describe—including retrieval, governance, orchestration, and agentic workflows—could, in principle, be replicated using equivalent components on other cloud platforms. Our view is that Oracle’s offering provides the greatest alignment with the data security, integration, and functional needs of regulated institutions.
Before exploring the use cases in detail, defining the solution components that make up the Revvence LLM Fabric is helpful. This is not a single system or tool, but a coordinated architecture that integrates people, technology, and governance into a cohesive AI capability.
In highly regulated environments, trust in AI-generated outputs must be earned, not assumed. That is why the Revvence LLM Fabric includes an advanced evaluation mechanism known as LLM-as-a-Judge: the use of a second model to critically assess the relevance, quality, and compliance of outputs generated by the primary model.
This approach supports banking-specific requirements such as:
How it Works
Example Use Cases
LLM-as-a-Judge does not replace human sign-off — it enhances human oversight by pre-filtering, contextualising, and evaluating generated content at scale. It introduces a structured, scalable review mechanism that accelerates assurance without compromising governance.
Together, these components form a structured, enterprise-ready architecture that reflects the real operational needs of financial institutions. Each capability described here has been validated through existing Oracle Cloud Infrastructure services or product features, making the Revvence LLM Fabric not aspirational, but a viable approach.
As currently envisioned, the Revvence LLM Fabric is not intended to be exposed to business users as a generic chatbot or embedded copilot within every enterprise application. Instead, it would be surfaced through carefully designed access points that align with existing workflows, systems, and user roles.
These interaction models are part of our architectural vision and represent how we anticipate users would engage with the Fabric as it matures and is implemented in practice.
Business users — such as finance leads, risk managers, and ESG officers — would typically engage with the Fabric through dedicated companion interfaces. These might take the form of:
These interfaces are not general-purpose chatbots. They are structured access points envisioned for specific use cases and user roles, without requiring users to directly engage with the underlying AI models, retrieval processes, or governance mechanisms.
In many cases, we envisage the Fabric being invoked automatically as part of a system workflow:
These examples represent potential intelligent automation scenarios, showing how the LLM Fabric could eventually augment structured workflows without manual prompting, assuming the appropriate governance and integrations are in place.
For technical users (e.g., solution architects, AI engineers), Oracle OCI offers tools such as model playgrounds, tuning environments, and prompt templates. These would be used to fine-tune models, integrate with vector databases, and build orchestration pipelines.
Subject matter experts (SMEs) would interact with the Fabric via review and approval interfaces. They would not manage prompts but provide oversight on generated outputs, guided by embedded LLM-as-a-Judge evaluation mechanisms.
In more advanced use cases, SMES working on complex scenario design or stress testing could also request support through a simulation assistant, prompting the system to surface prior assumptions, similar macroeconomic conditions, and previously approved rationale from capital planning or ICAAP documentation. This improves the efficiency, traceability, and credibility of scenario development workflows.
In sum, we anticipate that the Revvence LLM Fabric will support multiple, role-specific interfaces, not a one-size-fits-all chatbot. This design is intended to allow the Fabric to scale thoughtfully across business functions while upholding governance, security, and contextual integrity as foundational principles.
The true value of the Revvence LLM Fabric lies not in point solutions, but in the strategic transformation of how banks govern, reason, and execute across finance, risk, treasury, and sustainability. It enables high-leverage opportunities that extend beyond automation to reshape institutional reasoning, assurance, and agility.
Deploy intelligent agents to continuously interpret and align with evolving regulatory guidance (e.g. CRR, CSRD, Basel IV, EBA publications). LLMs can generate change impact assessments, track inconsistencies with internal policy, and support faster operationalisation of new expectations.
Apply consistent reasoning, tone, and justification across capital plans, ICAAP narratives, ESG disclosures, and internal board communications. LLM-as-a-Judge ensures alignment with policy and precedent, allowing SMEs to approve rather than author.
Link board-level strategic assumptions to forecast outputs and final disclosures. The LLM Fabric supports traceable narrative chains across capital, liquidity, and cost planning workflows, improving explainability and response readiness.
Embed AI agents to monitor reconciliation quality, ESG classifications, control adherence, and consistency of disclosure. Exceptions are flagged automatically, reducing manual checks and enhancing first-line assurance.
Enable business users to interrogate planning, risk, and regulatory data using natural language, drawing on historical submissions, scenario assumptions, and internal policy. This will accelerate insight generation and support strategic decision-making.
These higher-level opportunities align with broader industry thinking from firms like McKinsey and Accenture, emphasising LLMs’ potential for enterprise-wide transformation.
The capabilities of the LLM Fabric extend far beyond conventional generative AI. Based on challenges shared by institutions during recent innovation and AI forums, several high-impact areas have emerged that showcase where the Fabric can drive measurable transformation. These include:
Banks often struggle to align new stress scenarios with historic regulatory positions or internal assumptions. LLM agents can compare new proposals against ICAAP narratives, climate stress models, or macroeconomic drivers, surfacing gaps or recommending revisions that align with past precedent.
Board-level planning cycles often involve repetitive narrative development that differs little year to year. The Fabric can retrieve and adapt previous capital, cost, or liquidity justifications, anchored in current plan data, while flagging inconsistencies for SME review.
Operational and reporting teams frequently wonder how new rules (e.g., EBA guidance or Basel updates) apply to their domain. LLM interfaces can explain regulatory policies in plain language and assess their intersection with the bank’s internal policy set, providing auditable explanations that accelerate change management.
Many sustainability teams find it challenging to generate consistent, explainable, and scalable DNSH and EU Taxonomy justifications. LLMs can classify disclosure elements, retrieve past justifications, and generate consistent rationale text while logging SME overrides and regulatory context.
Planning, regulatory, and ESG narratives often diverge across departments or systems. LLM-as-a-Judge can compare related outputs (e.g. sustainability plans vs. capital disclosures) and highlight misalignment in assumptions, definitions, or language tone.
The LLM Fabric can ingest structured forecasts from planning systems and generate detailed commentary, offering interpretive analysis grounded in past board packs, stress testing documentation, and ICAAP assumptions.
LLMs can support the development of reusable, well-documented stress testing model templates — particularly in Python — to assist with regulatory requirements such as CCAR or ICAAP. While not a replacement for risk modellers or economists, LLMs can help:
This enables faster model prototyping, improved transparency, and a more efficient review loop between quantitative teams and risk governance within the Revvence LLM Fabric.
Agentic RAG agents can continuously monitor updates from regulators and compare them to internal policies. Banks can receive alerts and suggested redlines when a policy change may necessitate an update to disclosure language or capital allocation methodology.
Instead of manually writing reconciliation commentary, LLMs can analyse closing entries and system variances, generate initial explanations, and flag anomalies for SME approval.
LLMs can help explain cost movements across business units or functions, based on structured budget data and policy-based logic, providing a narrative suitable for internal presentation or statutory context.
Using RAG and agentic workflows, LLMs can assess assets for taxonomy alignment and generate consistent DNSH justification text, reducing manual workload and improving consistency across submissions.
The Revvence LLM Fabric™ offers a disciplined, architectural approach to applying large language models across the most complex, sensitive, and regulated areas of banking.
It avoids the common pitfalls of unchecked automation or relying on novelty for impact. Instead, it represents a shift toward institutional reasoning — where AI becomes a transparent, controlled, and integrated layer within the bank’s operating model.
By structuring the LLM capability as a semantic and orchestration layer — governed, retrievable, and agent-ready — the Fabric enables consistency, traceability, and responsiveness across capital planning, scenario analysis, regulatory interpretation, and ESG reporting. This approach reduces duplication, builds trust, and accelerates time-to-value.
Critically, we do not advocate a monolithic deployment. The Revvence LLM Fabric can be implemented incrementally — starting with retrieval-augmented generation in specific domains, or deploying LLM-as-a-Judge as a lightweight quality assurance overlay. It grows with the organisation’s maturity in GenAI governance and its appetite for innovation in high-trust environments.
To explore how this approach could be tailored to your institution, we recommend engaging with a Revvence Innovation Lab. These are short, focused sprints designed to prototype specific use cases, such as capital planning narratives, ICAAP justification reviews, or ESG classification workflows, using your existing systems and internal artefacts. Each lab concludes with a defined architecture, governance outline, and feasibility assessment.
The Fabric offers a practical path forward for financial institutions looking to move beyond experimentation and into meaningful, model-governed automation..
Revvence can help in several valuable ways: