Sagar Gupta’s RAMAS introduces AI-powered, audit-grade financial analysis with retrieval-based accuracy and transparency.
Sagar Gupta
Interviewer: With AI redefining every industry, finance remains one of the most complex frontiers for automation. Sagar Gupta - IT Solutions Manager at EST03 Inc. USA, Forbes Technology Council member, and recent author in the Journal of Computational Analysis and Applications - has introduced a pioneering framework called the Retrieval-Augmented Multi-Agent System (RAMAS) for financial statement analysis. We sat down with him to unpack how his research could reshape financial auditing, compliance, and analytical transparency across the corporate world.
Q: Sagar, congratulations on your publication. Let’s start at the beginning - what inspired the creation of RAMAS?
Sagar Gupta:
Thank you. The idea really came from observing the limitations of today’s AI systems in handling financial data. Financial statements are dynamic - they change quarter to quarter, company to company, and industry to industry. Traditional AI models often “hallucinate” or guess when they lack factual data.
I wanted to build a system that doesn’t just generate answers, but retrieves evidence, verifies sources, and explains its reasoning. RAMAS - which stands for Retrieval-Augmented Multi-Agent System - does exactly that. It combines retrieval-based AI with multiple specialized agents that work like a digital team: one retrieves facts, one analyzes ratios, one audits the outputs, and one compiles the report. It’s like having a team of AI analysts and auditors working under a single controller.
Q: That sounds revolutionary. Can you explain how these “agents” collaborate inside RAMAS?
Sagar Gupta:
Sure. Imagine you ask RAMAS: “Compare the gross margins of Apple and Microsoft between 2021 and 2024, and explain the drivers behind changes.”
The system breaks this into smaller tasks. The Controller Agent plans the workflow. The Retrieval Agent searches thousands of SEC filings - those XBRL/iXBRL documents that contain detailed financial facts. Then the Analysis Agents compute ratios, trends, and even extract qualitative insights from Management Discussion & Analysis (MD&A) sections. Finally, the Auditor Agent checks every number to ensure it’s sourced and cited correctly.
The result is a fully traceable financial analysis, with every statement linked back to the official filing line or table - something even many human analysts struggle to maintain consistently.
Q: You emphasize “retrieval” a lot. Why is retrieval so critical in this AI era?
Sagar Gupta:
Because facts in finance are not static - they’re timestamped, regulated, and auditable. A model that “remembers” data from 2022 but doesn’t know about a 2024 restatement is dangerous.
Retrieval ensures the AI always grounds its answers in the latest available filings. It accesses data directly from regulatory sources like the SEC’s EDGAR system, processes machine-readable XBRL data, and uses hybrid retrieval techniques - both keyword-based (BM25) and semantic (dense embeddings). That’s how RAMAS stays accurate, current, and explainable.
Q: What differentiates your approach from earlier financial AI models?
Sagar Gupta:
Most previous models focused either on numerical reasoning or natural language summarization, not both. Financial data is hybrid - tables, footnotes, narratives, and even images. RAMAS handles this complexity by orchestrating multiple AI agents that specialize in specific reasoning types - like ratio computation, risk commentary, or text-table synthesis.
We also introduced an “Auditor Agent” that acts as an internal regulator. It enforces source attribution and ensures that every numeric claim is backed by evidence. In our tests, removing this agent drastically reduced accuracy and reliability - just like removing a human auditor from a financial process.
Q: How do you measure the system’s performance?
Sagar Gupta:
We evaluated it using standard benchmarks like FinQA (for financial reasoning) and TAT-QA (for text-and-table question answering). We also applied retrieval metrics such as nDCG@10 and Recall@k, and tracked metrics like factual F1, latency, and audit violations.
Even in synthetic tests, the system showed remarkable consistency - when the auditor and reranking components were removed, factual accuracy dropped significantly. That told us the framework wasn’t just working - it was learning responsibly.
Q: Financial data is highly regulated. How does RAMAS ensure compliance?
Sagar Gupta:
Compliance is built into the architecture. Every number or quote is tied to a specific document ID, section, and taxonomy element - whether it’s US-GAAP or IFRS.
We also log every agent’s conversation and tool call for auditability. This makes the system transparent enough for enterprise and regulator adoption. In fact, the model explicitly avoids extrapolating beyond filed data, which aligns with financial reporting ethics and regulatory standards.
Q: What about future applications? Where do you see RAMAS heading next?
Sagar Gupta:
The foundation is in financial statement analysis, but the broader vision is AI-augmented financial governance. Imagine auditors, CFOs, or investors getting on-demand, explainable analytics across 10-Ks, ESG reports, or even private filings - all with citations and reasoning trails.
We’re now exploring how RAMAS could integrate with ERP systems like NetSuite or SAP to offer real-time, AI-driven insights - from revenue forecasting to credit risk analysis - without compromising auditability.
Q: What challenges did you face while developing this system?
Sagar Gupta:
One challenge was balancing accuracy with performance. Multi-agent systems can be computationally heavy. We had to design caching and “early-exit” strategies to maintain speed.
Another challenge was handling non-standard data - tables embedded as images or inconsistent taxonomies from international filers. We used OCR and taxonomy mapping to overcome that, but there’s still work to be done to handle global variations more efficiently.
Q: You’ve merged deep AI research with practical enterprise applications. How do you bridge that gap?
Sagar Gupta:
That’s a great question. My work at EST03 involves real-world ERP and financial systems, so I see the operational pain points firsthand. My research, on the other hand, ensures we’re building on scientific rigor.
RAMAS represents that bridge - it’s not just an academic experiment, it’s an enterprise-ready architecture that can actually reduce audit risk, improve transparency, and scale across organizations.
Q: Finally, how do you see AI transforming the role of financial analysts and auditors in the next decade?
Sagar Gupta:
AI won’t replace financial analysts - it will elevate them. Analysts will spend less time reconciling numbers and more time interpreting insights. The role will shift from data crunching to strategic storytelling.
In that sense, systems like RAMAS aren’t about automation - they’re about augmentation. They bring factual grounding, consistency, and explainability, freeing human experts to focus on judgment, creativity, and decision-making.
Closing Thoughts
Sagar Gupta’s Retrieval-Augmented Multi-Agent System (RAMAS) represents more than a technical innovation - it’s a vision for responsible AI in finance. By merging retrieval-grounded intelligence with audit-grade transparency, Gupta’s work bridges academia and enterprise, offering a roadmap for how future financial systems might think, reason, and explain like humans - only faster, and with citations.
Subscribe today by clicking the link and stay updated with the latest news!" Click here!
