Banks and insurers are beginning to ask a simple but transformative question: what if you didn’t need a report to get an answer?
As enterprise data becomes more accessible through AI and Large Language Models, the traditional model of static dashboards and report packs is being challenged by something more dynamic—conversational access.
This blog explores what happens when internal reporting becomes interactive.
It shows how AI—particularly Generative AI—combined with systems like Oracle EPM can enable teams to “talk” to their data, freeing up time and delivering faster, more intuitive access to insight. And it outlines why now is the right moment to reimagine how internal reporting could be completely transformed.
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.
In banks and insurance companies, internal reporting has long been the backbone and bottleneck of operational insight. Month-end close cycles produce a vast array of dashboards, KPI trackers, flash reports, and executive summaries. Risk teams generate outputs from regulatory stress tests and credit risk dashboards. Treasury creates daily liquidity and interest rate sensitivity reports. ESG and finance departments prepare disclosure packs that are aligned with evolving regulations and standards.
What binds all of these together? A reliance on static reporting tools, manual navigation, and a persistent hope that the right stakeholder finds the right insight at the right time.
But the real world doesn’t operate that way. Insights often become buried. Dashboards multiply without clear ownership. Business users feel overwhelmed by volume and underwhelmed by usability. Reports intended to provide clarity frequently don't aid in decision-making.
What if your head of FP&A, sustainability lead, or capital planning analyst could simply ask a question and talk with your data:
“What’s our latest liquidity buffer? How did our CET1 ratio move after the dividend planning adjustment? What’s changed in our emissions impact since our last EU Taxonomy alignment update?”—and receive a tailored, contextual answer immediately?
For the remainder of this blog, we explore a Revvence Recipe to deliver the vision of conversational access to internal data. And how the Oracle Digital Assistant (ODA), combined with Generative AI, is poised to make it a reality.
The potential of conversational interfaces isn’t new. However, recent advancements in large language models (LLMs), retrieval-augmented generation (RAG), and document understanding are shifting the landscape. These technologies are now sufficiently advanced to interact securely and effectively with enterprise data.
ODA offers an AI-powered interface that can connect to internal systems, understand natural language queries, and return responses drawn from structured sources like Oracle EPM and unstructured inputs like regulatory PDFs or policy documents. Importantly, it maintains context throughout a session, allowing for deeper, multi-step exploration.
This shift from "push reporting" (where data is periodically served up) to "pull insights" (where data responds dynamically to questions) could reshape the way internal stakeholders engage with enterprise data.
In many finance and risk functions, report sprawl is now a risk in its own right. Some banks maintain hundreds—even thousands—of active reports. Each exists for a reason, but few are regularly reviewed. Data environments become more complex, and disclosure requirements evolve, creating operational drag.
Conversational access to data solves several real-world pain points:
Rather than replacing reports entirely, ODA acts as a front door to your data ecosystem. It reduces dependency on predefined outputs and creates a more fluid way for teams to work with financial and risk data.
A head of FP&A at a retail bank might want to know how inflation assumptions impact the year's profitability projections. Instead of sourcing that from a planning model and translating it into a report, they ask:
“What’s the impact of updated inflation assumptions on full-year forecasted NII?”
ODA retrieves the relevant forecast drivers from the Oracle EPM scenario models and delivers a short, contextual explanation, drawing attention to shifts in margin, cost-of-funds, and customer behaviours.
In another case, an insurance FP&A lead could ask:
“How have claims volatility trends affected our latest solvency forecast?”
The assistant references actuarial planning data, links changes in assumptions to solvency KPIs, and offers the option to explore further by product line or region.
With the increasing complexity of ESG reporting—particularly under CSRD, EU Taxonomy, and PAI frameworks—internal sustainability teams are dealing with complex data, constant updates, and cross-functional coordination.
Using ODA, a sustainability reporting lead might ask:
“Summarise the key findings from our most recent financed emissions analysis.”
Or:
“Which parts of our Double Materiality Assessment flagged high reputational risk?”
ODA uses document understanding to interpret uploaded ESG reports and assessments, grounding answers in the specific language of the source material. The assistant also enables conversational navigation across frameworks—users can ask about taxonomy alignment, Scope 3 attribution, or climate transition planning metrics.
Capital and liquidity planning sits at the intersection of finance, treasury, and risk. These teams must navigate complex, forward-looking models while being ready to answer questions from executive committees or regulators.
A capital planning lead might ask:
“What is the projected CET1 impact of our base case dividend scenario?”
Instead of requesting a new report from finance, ODA retrieves forecast outputs, applies the logic of dividend policy to CET1 calculations, and returns the projected ratio with explanatory context.
A liquidity planning specialist could ask:
“Summarise our 30-day LCR forecast, including top three risk drivers.”
Behind the scenes, ODA interfaces with liquidity models, references stress scenarios, and delivers an executive-ready narrative aligned to internal and regulatory metrics.
The productivity gains from conversational reporting are not merely theoretical—they are measurable.
Consider a large bank or insurer with 200 to 300 full-time employees (FTEs) engaged in the reporting lifecycle. These roles encompass cost planning, ESG and capital reporting, financial planning, and IT operations.
Assuming an average fully loaded cost per FTE of £70,000 per year (a conservative estimate based on industry data for finance and compliance functions), the total spend on reporting-related talent could exceed £20 million annually.
Now consider the impact of minimising the time these teams dedicate to routine report creation, review, and synthesis. If conversational access to data can streamline 80% of that manual effort—by facilitating self-service insight generation, decreasing the need to construct and validate ad hoc reports, and removing duplication—the potential efficiency gains are substantial.
At the upper end of the estimate (300 FTEs):
Even at 50% of that figure, the business case for transformation is compelling—especially when the investment in AI infrastructure is relatively modest in comparison.
More importantly, this shift involves more than just cost reduction. It focuses on redeploying talent to higher-value activities.
Of course, deploying conversational access to data isn’t just a matter of turning it on. There are essential questions to navigate:
These are solvable challenges. Access control can be handled through existing identity and policy management tools. For example, ODA supports identity providers using OAuth2, OIDC, and SAML. RAG (retrieval-augmented generation) architectures to ensure that generative answers are grounded in real, verifiable content.
One of the most critical enablers of trust is the source of the data itself.
Since the primary system underlying these interactions is Oracle EPM—already the system of record for financial plans, forecasts, and capital models—users can rely on the integrity of the numbers. There is no 'shadow data' layer, and there are no approximations. The AI assistant draws directly from governed enterprise data that has already undergone validation, version control, and sign-off workflows.
This integration with Oracle EPM is not incidental—it’s essential. It means that conversational reporting does not require organisations to compromise on data quality or control. Adoption accelerates when the assistant proves it can save time and simplify complex tasks.
Some banks and insurers may also implement enterprise data strategies centred on cloud platforms like GCP or AWS, utilising tools like Qlik or Tableau to visualise and report on centralised data.
These strategies can coexist with conversational AI, particularly for aggregated insights across systems, but they introduce trade-offs. Data pipelines may create latency, and downstream reporting layers often do not carry forward the complete modelling logic, assumptions, or control structure of Oracle EPM. For finance users involved in scenario modelling, planning, or internal policy compliance, direct access to EPM remains essential.
And it’s not just numbers that users may want to query. Increasingly, there is interest in exploring the "why" behind the numbers—such as business rules, allocation models, driver trees, and policy assumptions.
Conversational interfaces can also support this. By indexing and grounding responses in metadata, rule documentation, and calculation logic from EPM, the assistant can provide values and the rationale behind them.
For example:
"How are building and facilities costs allocated to my business unit?"
Or
"What assumptions drive the CET1 calculation in this stress case?"
This elevates the assistant from a reporting tool to a training, audit, and compliance aid—bridging the knowledge gap between technical modellers and business users.
It’s easy to see where this leads. Rather than creating a custom dashboard for every recurring question, teams will develop a knowledge assistant trained on their data. Instead of sifting through endless reports, executives will converse with a digital analyst embedded in their EPM and risk management systems.
This doesn’t mean the end of reporting—it means a rebalancing. Structured reports will remain critical, but the dominant interface for many teams may shift from dashboards to dialogue.
And the business case is compelling. For institutions with hundreds of reporting professionals, even a partial shift toward conversational access can translate into millions of pounds in freed-up capacity. More importantly, it gives teams back the time and cognitive space to focus on high-value work—insight generation, scenario analysis, and decision support.
Banks and insurers that embrace this shift early will reduce the reporting burden and unlock a more natural, scalable way of engaging with their data and each other.
The technology is mature, and the data foundations are already in place. What is needed now is a willingness to reconsider how reporting is delivered, accessed, and experienced.
The question is no longer, “Can this work?” It’s “What are we waiting for?”
The Bottom Line: By adopting conversational AI now, banks and insurers can unlock substantial productivity gains, enhance the quality of internal decision-making, and reduce the operational burden of reporting. Early adopters are already reshaping how their organisations interact with data—moving from static outputs to fluid, intelligent dialogue. This shift isn’t just about efficiency—it’s about creating a more scalable, responsive, and insight-led enterprise.
At Revvence, we specialise in implementing Oracle’s leading-edge AI solutions for banks. With our expertise in risk, finance, and treasury, we help you deploy AI tailored to your specific challenges and goals. From pilot projects to full-scale deployments, we ensure that your journey with AI delivers measurable business impact.
Revvence can help in several valuable ways: