From Burden to Breakthrough: How Agentic AI Reinvents Risk and Regulatory Reporting

Agentic AI is how leading financial institutions turn risk aggregation and regulatory reporting from a slow, manual burden into a real‑time, always on advantage, boosting accuracy, slashing costs, and accelerating insight.

Dremio’s Agentic Lakehouse gives financial institutions the data foundation and AI agents they need to industrialize risk aggregation and regulatory reporting, with higher accuracy, lower cost, and faster time to insight.

Why risk aggregation and regulatory reporting need agentic AI

Global banks and insurers face a perfect storm: expanding regulatory requirements, growing data volumes, and expectations for near real‑time risk visibility across trading, credit, liquidity, climate, and operational risk. At the same time, regulators are tightening capital frameworks and scrutinizing model risk, data lineage, and governance, forcing institutions to modernize how they aggregate and report risk.

Agentic AI changes the game by deploying autonomous, task‑oriented AI agents that can discover, prepare, calculate, monitor, and explain risk metrics on top of governed data products, not siloed spreadsheets or opaque pipelines. When this is combined with a lakehouse architecture, risk and finance teams can move from overnight batch runs to near real‑time analytics and continuous compliance monitoring.

The global regulatory landscape and cost of non‑compliance

Financial institutions must comply with a dense mesh of global and local regulations that all depend on timely, accurate, and well‑governed risk data. Key frameworks include:

• Basel III and its finalization (“Basel III endgame”) for credit, market, counterparty, and operational risk capital, including standardized and internal model approaches.
• FRTB and related market risk rules, requiring desk‑level risk factor modellability, sensitivities, P&L attribution, and backtesting.
• Stress testing and capital planning regimes such as CCAR and DFAST in the US, PRA stress tests in the UK, and EBA exercises in the EU, all requiring granular, explainable risk aggregation under scenarios.
• Accounting and impairment standards such as IFRS 9 and CECL, which rely on forward‑looking credit risk models and lifetime expected loss calculations across portfolios.
• Liquidity, resolution, and G‑SIB regulations, climate and ESG reporting, and conduct/operational risk regimes that draw on shared data about exposures, customers, and processes.

Non‑compliance can lead to multi‑billion‑dollar fines, capital add‑ons, consent orders, and restrictions on growth, as well as reputational damage that erodes investor and customer confidence. Supervisors increasingly expect robust data lineage, explainable models, and near real‑time monitoring, so manual aggregation and siloed legacy platforms are no longer sustainable.

Typical data and process challenges

• Fragmented data across core banking, trading, treasury, finance, and risk platforms, often duplicated into multiple warehouses and marts.
• Batch‑driven ETL and overnight “aggregation factories” that make intra‑day or on‑demand risk views difficult.
• Point solutions for specific regulations, each with its own data copies, definitions, and controls, complicating consistency and governance.
• Manual processes for reconciliations, adjustments, and report compilation, increasing operational risk and slowing responses to regulator queries.

This is exactly the environment in which agentic AI, running on top of Dremio’s Agentic Lakehouse, can deliver outsized ROI.

How financial institutions are using agentic AI for risk and reporting

Global banks, insurers, and asset managers are already deploying agentic AI to automate complex financial workflows, including risk analytics and regulatory processes. Research and industry reports highlight several emerging usage patterns that are rapidly moving from pilots into production:

• Autonomous data discovery and quality checks: AI agents traverse catalogs, schemas, and logs to locate relevant positions, reference data, and market data; assess data quality; and propose or execute remediation actions.
• Dynamic risk aggregation: Agents orchestrate queries and calculations across multiple systems to produce consolidated views of credit, market, and liquidity risk at portfolio, desk, and legal‑entity levels, with drill‑downs.
• Scenario analysis and stress testing: Agents run Monte Carlo simulations, scenario shocks, and sensitivities at scale, then summarize impacts on capital and liquidity in language tailored to risk committees and regulators.
• Regulatory report drafting and validation: Agents compile data from governed sources, generate draft templates and narrative sections for regulatory filings, and highlight anomalies or rule breaches for human review.
• Continuous control monitoring: Agents watch key risk indicators and thresholds in near real‑time, triggering alerts, playbooks, or additional calculations when conditions change.

Industry surveys and conference reports show that large institutions across The Americas, Europe, and Asia are investing heavily in agentic AI to automate mission‑critical workloads, including risk management and regulatory compliance. Leading firms are expanding their AI risk frameworks to explicitly cover agents, adding guardrails around tool access, data governance, and human oversight so that autonomous workflows remain compliant and auditable.

The Agentic Lakehouse: why architecture matters

Agentic AI only delivers sustainable value if it runs on trusted, well‑governed, and highly performant data infrastructure. Dremio’s Agentic Lakehouse is designed precisely for this combination of unified data and intelligent agents, with several characteristics that are critical for risk and regulatory workloads:

• Unified but virtualized data: Dremio allows you to query data in place across data lakes and warehouses, avoiding proliferation of copies while still providing a single, logical view for risk and finance.
• Semantic lakehouse catalog: A central catalog of datasets and business views, with consistent naming, definitions, and relationships, means AI agents and humans share the same governed semantic layer.
• Fine‑grained governance and access control: Integration of Dremio’s AI Agent with the lakehouse catalog and governance framework ensures that every agent interaction respects policies, roles, and entitlements.
• High‑performance query and compute engine: Dremio’s query acceleration and elastic scaling support computationally intensive risk calculations, scenario runs, and intra‑day aggregation without fragile ETL chains.
• Open and hybrid: Dremio connects to on‑prem, private cloud, and public cloud data sources, supporting the hybrid reality of global institutions and the regulatory need to keep certain data in specific jurisdictions.

Because Dremio embeds AI capabilities directly in the lakehouse, institutions can deploy AI agents that understand the data catalog, lineage, and governance context, rather than bolting generic AI tools onto legacy warehouses.

Dremio’s AI Agent and AI Functions

Dremio’s AI Agent and AI Functions bring agentic behavior into the data plane, enabling intelligent automation of risk and regulatory workflows on top of governed datasets. Examples include:

• Conversational access to risk data: Risk analysts can ask natural‑language questions (for example, “Show 10‑day 99% VaR by desk for Q1 under stressed volatility”) and have the AI Agent translate them into optimized queries against curated views, returning both numbers and explanations.
• Reusable AI‑powered transformations: AI Functions encapsulate complex logic (for example, risk factor bucketing, PD/LGD segmentation, scenario generation) as reusable functions that can be composed into pipelines and invoked by agents.
• Automated aggregation and report preparation: Agents can orchestrate sequences of Dremio queries and AI Functions to produce regulatory‑grade aggregation tables, audit logs, and draft reports on demand or according to schedule.

By aligning AI Agents with the same governance, metadata, and controls as other Dremio workloads, Financial Institutions can safely expand autonomous use cases while preserving auditability and regulatory comfort.

Using Dremio’s Agentic Lakehouse for risk aggregation, calculation, and reporting

Dremio’s Agentic Lakehouse supports the full lifecycle: from ingest and aggregation, through calculation, to regulatory reporting and continuous monitoring.

1. Data unification and real‑time availability

Dremio connects directly to trading systems, core banking, collateral, treasury, market data feeds, reference data repositories, and data lakes without duplicating everything into yet another warehouse. Institutions can then define governed, virtualized “golden sources” for positions, exposures, customers, and reference data, maintained centrally in the lakehouse catalog.

AI agents can:

• Discover and classify risk‑relevant datasets across the catalog, tagging them with risk‑specific metadata such as exposure types, currencies, hierarchies, and regulatory mappings.
• Continuously profile data for completeness, timeliness, and quality issues, raising alerts and even proposing corrective transformations using AI Functions.

Dremio’s query engine supports both historical and near real‑time analytics over these unified views, which is crucial for intra‑day risk and liquidity monitoring.

2. Agent‑driven risk aggregation

Risk aggregation requires consistent hierarchies (legal entity, business line, desk, product), exposure metrics, and risk factor mappings across data from multiple systems. Dremio enables you to model these hierarchies as views in the lakehouse, which AI agents then use as building blocks for aggregation.

On top of Dremio, agents can:

• Generate and maintain standardized exposure measures such as EAD, EPE, and sensitivities across portfolios using AI Functions wrapped around existing quantitative libraries.
• Execute aggregation pipelines that roll exposures and risk measures up legal entity, geography, and product hierarchies with consistent filters and currency treatments.
• Compare multiple aggregation definitions (for example, regulatory vs internal economic views) and highlight differences that could cause reconciliation issues or regulatory findings.

Because these pipelines run directly on the agentic lakehouse, risk teams can re‑run or adjust aggregations interactively when regulators or internal committees request new cuts of the data.

3. High‑performance risk calculation

Risk engines and quantitative libraries can be integrated with Dremio so that AI agents orchestrate both data retrieval and computation. This allows Financial Institutions to industrialize complex calculations for:

• Market risk (VaR, ES, sensitivities, P&L attribution, FRTB metrics).
• Credit risk (PD, LGD, EAD, expected loss, RWA).
• Liquidity risk (LCR, NSFR, survival horizons, collateral and funding gaps).
• Stress testing and scenario analysis across macro, idiosyncratic, and climate scenarios.

Agents can schedule and distribute calculation jobs, monitor completion, evaluate results against thresholds or backtesting criteria, and generate exception reports for human review. They can also explain the drivers of changes in risk measures over time by querying historical data and summarizing key factors for management dashboards.

4. Regulatory reporting and documentation

Regulatory reporting is not just about numbers; it is about traceability from input data to reported figures, plus clear narrative explanations. Because Dremio’s Agentic Lakehouse keeps calculations close to governed data, AI agents can automatically generate much of the required documentation:

• Lineage views: Show how each metric in a report traces back to source datasets, transformations, and calculation logic in Dremio.
• Data and model glossaries: Compile up‑to‑date definitions of fields, models, and assumptions from the catalog into regulator‑ready documentation.
• Draft templates and narratives: Populate regulatory templates (for example, capital, liquidity, and risk disclosures) with Dremio‑derived data and generate draft narrative sections that risk teams can refine.

When regulators request ad‑hoc analyses or horizontal reviews, institutions can respond faster because agents can regenerate analysis and documentation directly from governed lakehouse views rather than rebuilding pipelines from scratch.

5. Continuous monitoring, controls, and explainability

Agentic AI on Dremio enables continuous risk and compliance monitoring, closing the gap between periodic reporting and real‑time operations.

• Agents watch key metrics and control checks (for example, data timeliness, limit breaches, threshold violations) and trigger workflows or alerts when anomalies occur.
• Governance policies in Dremio ensure that agents only see and act on data they are entitled to, with activity logs that support audits and model risk management.
• Explainability features, including lineage, semantic metadata, and agent‑generated summaries, which help model risk and internal audit teams understand how AI‑driven workflows influence risk and reporting.

This combination makes it easier to satisfy evolving supervisory expectations around AI governance while still capturing the productivity gains of agentic automation.

ROI and business impact for financial institutions

Institutions that adopt agentic AI on a modern lakehouse are reporting material productivity gains, faster time to market for new analytics, and better use of scarce risk and data talent. Dremio’s Agentic Lakehouse is purpose‑built to maximize that ROI while maintaining the controls needed in a regulated environment.

Value drivers with Dremio’s Agentic Lakehouse

By consolidating risk and regulatory workloads on Dremio’s Agentic Lakehouse, banks can reduce infrastructure and licensing costs, shorten reporting cycles, and improve the reliability and defensibility of their submissions. That translates into higher ROI, lower operational and regulatory risk, and more capacity for innovation in pricing, portfolio optimization, and customer analytics.