Operationalising LLMs Across the Bank: A Strategic Guide

How the Revvence LLM Fabric™ Enables Enterprise-Grade AI Across Risk, Finance, and Sustainability.

The Moment of Opportunity.

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.

Jump to Section

1. Introducing the Revvence LLM Fabric™: A New Strategic Architecture.
2. The Current Landscape: Point Solutions vs Enterprise Fabric.
3. Solution Overview of the Revvence LLM Fabric.
4. How Users Would Interact with the Revvence LLM Fabric.
5. Use Cases Enabled by the Revvence LLM Fabric.
6. How can we help?

Introducing the Revvence LLM Fabric™: A New Strategic Architecture.

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:

• A governed retrieval layer, which indexes and manages internal documentation, submissions, and regulatory artefacts
• An integration layer, where responses are routed through APIs, workflow tools, and enterprise applications
• A tools and action layer, which enables the invocation of agents, APIs, and execution routines based on user input, retrieved context, or system events
• A governance framework that ensures outputs are explainable, reviewed, and auditable

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.

The Current Landscape: Point Solutions vs Enterprise Fabric.

Today, many banks are already exposed to generative AI through enterprise tooling:

• Microsoft Copilot integrates summarisation and generation into Excel, Outlook, and PowerPoint.
• Google Workspace AI assists in drafting content across Docs and Sheets.
• AWS Bedrock and Azure OpenAI allow selective experimentation with hosted LLM endpoints.
• Core enterprise platforms such as Oracle EPM and Oracle Cloud ERP are embedding AI and generative AI capabilities directly into their feature sets, enabling contextual assistance, narrative generation, and smart analysis inside planning, reporting, and operational workflows.

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.

This distinction between personal productivity and enterprise transformation is critical. Where Co-Pilots support the individual, the LLM Fabric supports the institution.

Solution Overview of the Revvence LLM Fabric.

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.

Core Components of the Revvence LLM Fabric.

1. Internal Knowledge Layer (Retrieval Foundation)

• This central content layer powers all reasoning and generation capabilities within the LLM Fabric. It includes all the internal documentation that banks rely on to interpret, justify, and communicate decisions, such as regulatory submissions, stress test artefacts, policies, process notes, SME commentary, and reconciliation justifications.
• Managed using Oracle Object Storage and Oracle OCI Data Catalog, integrated with OCI Vector Database for secure, high-speed semantic retrieval. This setup enables RAG-based workflows that are fully private and auditable.- Maintained within secure, bank-resident infrastructure (e.g., Oracle Object Store + Vector DB).
  
  

2. Model Layer (LLM Engine)

• This is the reasoning and language generation engine at the core of the Fabric. It enables interpreting complex regulatory language, generating narrative content, and classifying inputs in alignment with internal frameworks. Technical teams, such as AI engineers or solution architects, operate and manage this layer, which configures prompts, tunes model performance, and integrates models into enterprise systems.
• Hosted private models using Oracle Generative AI, which provides access to production-grade large language models developed by Cohere, delivered natively within OCI.
• Also compatible with open-source models deployed in the customer's own tenancy for specialised tasks, supporting prompt engineering, chaining, and integration with retrieval and governance layers.
• Built on OCI AI Services with prompt templates, fine-tuning support, and compatibility with retrieval layers at the core of the Fabric. It enables interpreting complex regulatory language, generating narrative content, and classifying inputs in alignment with internal frameworks.
• Hosted private models using Oracle Generative AI, which provides access to production-grade large language models developed by Cohere, delivered natively within OCI.
• Also compatible with open-source models deployed in the bank’s own tenancy for specialised tasks, supporting prompt engineering, chaining, and integration with retrieval and governance layers. or integration with open models deployed within OCI.
• Built on OCI AI Services with prompt templates, fine-tuning support, and compatibility with retrieval layers.
• Prompt engineering and chaining built around specific use cases (narrative generation, classification, reasoning).
  
  

3. Integration Layer (Application Embedding)

• The Integration Layer enables the LLM Fabric to securely connect with enterprise systems such as Oracle EPM, Oracle Financial Analytics (FAW), Oracle Cloud ERP, and ESG platforms. This connection is typically established through APIs, event triggers, or data pipelines that expose key inputs and allow model-generated responses to be returned in structured formats.
• In practice, this means the Fabric can retrieve structured plan data from Oracle EPM to support forecasting commentary generation, pull actuals from Oracle ERP to explain cost variances, or analyse ESG disclosures for alignment with internal sustainability frameworks.
• Rather than embedding LLMs directly into the EPM or ERP web interfaces, Fabric capabilities are typically surfaced through adjacent, secure interfaces — such as domain-specific assistants or workspaces — which draw on live or batch data. This preserves platform stability and aligns with role-based access controls.
• The result is a tightly coupled yet modular augmentation model, where the LLM Fabric enhances financial and regulatory processes without compromising the integrity or control of core transactional systems.
  
  

4. Orchestration and Agent Layer

• This layer enables the coordination of multi-step workflows that require retrieval, generation, evaluation, and decision support. It underpins the shift from passive content generation to active enterprise process automation.
• RAG pipelines (retrieval-augmented generation) are configured to query vectorised internal corpora, retrieve relevant documentation, and pass context to the model layer for generation or evaluation. Agentic RAG extends this by orchestrating multi-step tasks, such as comparative analysis, policy alignment checks, or automated commentary generation, and integrating them into defined decision workflows.
• Oracle’s AI Agent Studio and Fusion AI Agents provide a strong foundation for these capabilities, allowing organisations to build and govern domain-specific AI agents that are embedded in core business processes. These agents can observe events (e.g. a change in a cost centre forecast or a regulatory policy update), reason across structured and unstructured inputs, generate context-aware responses, and route outputs through SME or compliance approvals.
• Built on top of Oracle Cloud Infrastructure’s Data Flow (for orchestration), Functions (for event-based triggers), and API Gateway (for secure model interaction), these agents support use cases across finance, risk, treasury, and sustainability with enterprise-grade auditability.
• This capability allows the LLM Fabric to automate complex decision chains—for example, generating scenario narratives, validating consistency with internal policy, requesting SME sign-off, and publishing commentary to EPM or ESG systems—all through a secure and repeatable process.
  
  

5. Governance and Control Layer

• This layer defines how the LLM Fabric maintains trust, oversight, and explainability in all outputs. It includes role-based access controls, audit trails, human-in-the-loop (HITL) checkpoints, and model evaluation protocols to ensure business users retain final accountability.
• Oracle OCI supports this through Identity and Access Management (IAM), Logging and Monitoring services, and API Gateway policies that restrict how and when models are accessed.
• Evaluation mechanisms such as LLM-as-a-Judge (see below for additional details) are applied to assess generated outputs’ relevance, consistency, and regulatory compliance before SME signoff or publication. These can operate autonomously or serve as secondary review agents that compare responses against predefined templates, past submissions, or key policy criteria.
• This governance model allows institutions to benefit from speed and scale without sacrificing control or compliance alignment.
  
  

6. Feedback and Tuning Loop

• The LLM Fabric incorporates structured mechanisms for capturing SME feedback on generated responses — both at runtime (e.g., post-review comments) and over time (e.g., trend-level commentary on output quality).
• This feedback is used to iteratively improve system behaviour through prompt refinement, model selection, vector index tuning, or the retraining of logic chains in the agent layer.
• OCI Generative AI allows fine-tuning of custom models using enterprise-specific datasets stored securely in Oracle Object Storage. This means banks can incrementally adapt models to reflect institutional tone, regulatory preferences, or sector-specific nuances.
• In cases where fine-tuning is not necessary, adjustments to retrieval scope, ranking weights, or embedding logic can materially improve performance, ensuring continuous optimisation without full retraining, and incorporating it into future tuning, retrieval configuration, or system logic refinements.
• Enables continuous improvement through in-context learning, without full model retraining.
• Enables continuous improvement without full retraining.

7. LLM-as-a-Judge

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:

• Consistency with past submissions (e.g. ICAAP disclosures, board reports, or regulatory narratives)
• Tone, structure, and policy alignment with internal governance frameworks
• Accuracy against data sources, especially when grounded generation is used via RAG

How it Works

• After a draft output is generated — for example, a capital planning rationale or DNSH justification — it is passed to a second LLM, configured to act as a “reviewer.”
• The Judge model compares the output to reference materials: past narratives, SME-authored templates, internal policy wording, or structured data points.
• It scores or annotates the draft for relevance, alignment, clarity, or tone — and can either return it for revision, suggest edits, or allow it to move forward with human review.

Example Use Cases

• Compare a new capital adequacy statement to last year’s ICAAP justification, flagging differences in macroeconomic rationale.
• Evaluate a set of ESG disclosures for consistency of DNSH phrasing across similar asset classes.
• Score multiple scenario narratives against a pre-defined tone or policy checklist, allowing risk SMEs to review only low-scoring ones.
  
  

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.

How Users Would Interact with the Revvence LLM Fabric.

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.

Companion Interfaces for Business Users

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:

• A secure web-based workspace or portal where users could input prompts (e.g., request scenario narrative, retrieve past justification) and receive contextual responses
• A guided assistant embedded within or adjacent to planning and reporting dashboards, designed to answer specific queries or offer first-draft generation
• A domain-specific chatbot interface — drawing inspiration from our "Talking with Your Data" concept outlined in a separate blog — that allows users to explore internal data, interrogate historical narratives, or ask policy-related questions in natural language
• Embedded review panels within business applications that present model-generated suggestions or narrative explanations during formal planning or reporting workflows, allowing users to approve, revise, or flag content for SME review

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.

Triggered Interactions Within Enterprise Workflows

In many cases, we envisage the Fabric being invoked automatically as part of a system workflow:

• At the end of a financial planning cycle, it might assist with narrative generation based on system outputs
• During regulatory forecasting, it could flag inconsistencies with past submissions or generate updated commentary aligned with new assumptions
• In ESG reporting, it might classify assets and draft DNSH text as part of a taxonomy mapping tool
• In capital planning, it could automatically produce weekly or monthly advisory memos that summarise key narrative deviations, regulatory commentary gaps, or assumption shifts — helping planning and risk teams pre-empt review issues

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.

SME and Technical User Interfaces

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.

Use Cases Enabled by the Revvence LLM Fabric.

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.

Regulatory Intelligence at Scale

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.

Cross-Process Narrative Governance

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.

Front-to-Back Planning Traceability

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.

Agentic Monitoring and Assurance

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.

Conversational Knowledge and Decision Support

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:

Intelligent Scenario Validation and Alignment

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.

Contextual Reuse of Strategic Commentary

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.

Guided Policy Interpretation for Business Users

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.

ESG Data Rationalisation and Justification at Scale

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.

Cross-System Consistency Checking

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.

Capital and Liquidity Forecasting with Narrative Co-Pilots

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.

Scenario Modelling and Risk Planning

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:

• Generate structured, auditable code templates for scenario simulation and macroeconomic variable projection
• Document assumptions, methodologies, and parameter choices in human-readable formats alongside the code
• Compare versions of models and highlight material logic or input changes
• Summarise the intent and limitations of each model for business stakeholders

This enables faster model prototyping, improved transparency, and a more efficient review loop between quantitative teams and risk governance within the Revvence LLM Fabric.

Regulatory Policy Interpretation

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.

Financial Close Justification and Reconciliation Explanation

Instead of manually writing reconciliation commentary, LLMs can analyse closing entries and system variances, generate initial explanations, and flag anomalies for SME approval.

Cost Allocation and Planning Commentary

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.

ESG Mapping and Classification

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.

Conclusion: Turning Strategy into Impact.

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..

How can we help?

Revvence can help in several valuable ways:

• Check out Revvy, our Narrow-GPT for Finance Transformation. Read all about Revvy here.
• Review one of your existing finance processes to recommend where AI capabilities will have the most impact.
• Conduct one of our Innovation Labs (a free three-hour workshop) to show you the art of the possible and help you build your business case for change.
• The design and delivery of end-to-end solutions.