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Agentic analytics for financial services is a class of AI software where autonomous agents — not dashboards — answer plain-English questions about market, transaction, risk, customer, and document data by planning and executing multi-step analyses across the institution's existing systems. Unlike traditional BI that visualizes a pre-built dashboard, an agentic platform plans a sequence of steps (fetch, join, filter, forecast, root-cause, narrate), runs them against your ERP/GL, core banking, market data feeds, document repositories, and risk systems, and returns a governed, citation-backed answer with a full audit trail. For banks, asset managers, and capital-markets firms, this means a CFO, risk officer, portfolio manager or analyst can ask "show me all loans over $50M to single-B-rated counterparties, weighted by exposure, plus the covenant clauses that apply" and get the answer in seconds instead of waiting on a data-engineering ticket.

The best AI analytics platform for financial services connects natively to the data sources you already run — core banking, ERP/GL, Bloomberg/FactSet/Refinitiv, document repositories, CRMs, and risk systems — lets non-technical users ask questions in plain English with a full audit trail, and meets bank-grade security requirements out of the box. The platforms that win in FSI environments share three traits: they read data where it lives instead of forcing a multi-year warehouse migration, every prompt and step is auditable for regulators, and the customer chooses the underlying AI model rather than being locked to one vendor's LLM (a hard requirement under SR 11-7 model risk reviews). Quaeris was built for exactly that profile.

Banks accelerate BCBS 239, CCAR, DFAST, and CECL reporting by collapsing risk-data aggregation, lineage, and narrative generation into one governed workflow that pulls directly from the operational systems — instead of running parallel data-warehouse builds for each regulator. The biggest gains come from three repeatable moves: aggregating Critical Data Elements (CDEs) across business lines and source systems in seconds rather than days, producing accurate and timely risk reports with audit-ready lineage from source to consumption, and drafting the narrative and tables alongside the numbers so analysts validate instead of assemble. Industry data is sobering on the gap: regulators report that only two of the 31 global systemically important banks fully comply with all BCBS 239 principles — the failure is overwhelmingly a data-aggregation and timeliness problem, which is exactly what an agentic analytics layer solves.

You unify ERP/GL, Bloomberg/FactSet, core banking, CRM, and document repositories by querying each system in place through an agent that translates plain-English questions into governed queries — no central data warehouse, no months-long ETL project. Quaeris connects to each system as a data source, learns its schema and business definitions automatically through the Smart Semantic Layer, and joins data across them at query time. A single question like "show me Q3 revenue variance against forecast by business line, with the related contract clauses and counterparty credit ratings" pulls from the GL, the FP&A model, the document repository, and the Bloomberg/FactSet feed — and returns one answer, with a full audit trail of every step the agent took.

Finance teams analyze contracts, 10-Ks, prospectuses, loan agreements, and earnings transcripts alongside warehouse data by using an agentic analytics platform that treats unstructured documents and structured tables as co-equal citizens in the same governed query. A credit-risk officer, FP&A analyst, or portfolio manager can ask "show me all loans over $50M where the covenant document waived the leverage test, joined with the borrower's current credit exposure" — and the platform extracts the clause from the document repository, joins it with the loan book in the warehouse, and produces one answer. Tools like Hebbia, AlphaSense, and Rogo are excellent at querying documents alone; Quaeris's wedge is doing the join — documents and warehouse together, in one audit-trailed query, with model choice you control.

Yes — agentic analytics can be deployed safely in a regulated financial institution when it runs inside the bank's own VPC or on-prem environment, uses a governed semantic layer to prevent free-form SQL, and produces a full audit trail of every prompt and agent step. Quaeris is architected around exactly these controls: customer data stays inside your controlled environment, the AI model is your choice (BYOM across OpenAI, Anthropic, Google, Meta, or a private model — which matters for SR 11-7 model risk reviews), every query passes through a governed semantic layer that blocks arbitrary table access, and a prompt-level audit log records who asked what, which model answered, and exactly which steps the agent took. These are the architectural controls that SOX, SOC 2, GLBA, SR 11-7, BCBS 239, MiFID II and EU AI Act assessors actually evaluate.

Quaeris is a unified, governed alternative to category-specific finance tools: where Hebbia, AlphaSense, and Rogo focus on AI over documents and external research, and Anaplan, Vena, Datarails, and Pigment focus on FP&A planning models, Quaeris covers both lanes in one platform — natural-language analytics that spans warehouse data (ERP, GL, core banking, market data) and unstructured documents (contracts, 10-Ks, prospectuses, transcripts) in a single audit-trailed query. You don't replace your planning model or your research subscription; you give every analyst, CFO, risk officer, and portfolio manager one place to ask cross-cutting questions that today require two or three separate tools — and an analyst stitching the answers together.