From Hype to High ROI: How Dremio Supercharges AI in Financial Services

Agentic AI is Front and Center for Financial Services Strategy

Agentic AI is now at the center of competitive strategy in financial services, moving from pilots to production at scale. In this landscape, Dremio’s lakehouse-first approach gives banks, insurers, and wealth managers the data foundation they need to operationalize AI quickly and safely.

AI Trends Reshaping Financial Services

Several powerful themes are converging across banking, capital markets, and insurance:

• AI is becoming agentic: Financial institutions are building autonomous agents that can reason over complex data, trigger workflows, and continuously learn in areas like servicing, operations, and risk.
• Hyper‑personalization is now expected: Customers and advisors want offers, portfolios, and advice tuned to behavior, life stage, and real-time context - not static segments.
• Real‑time risk and fraud are non‑negotiable: Institutions need to detect anomalies as they happen, aggregate exposures intraday, and respond to evolving regulatory demands.
• Data is the main bottleneck: Most firms struggle more with fragmented, slow, costly data environments than with AI models themselves.

To capitalize on these trends, financial institutions need a modern data and AI foundation that is open, fast, governed, and cost‑efficient. That is where Dremio comes in.

How Dremio Unlocks AI for Financial Services

Dremio is an intelligent lakehouse platform that lets you run high‑performance analytics and AI directly on your data lake, without endlessly copying data into multiple warehouses or marts.

Key capabilities that matter for financial services:

• Unified access to all data: Query cloud object storage, databases, and streaming sources through a single SQL and semantic layer.
• No‑copy architecture: Minimize data duplication and ETL sprawl while still delivering interactive performance.
• Built‑in governance: Apply fine‑grained security, lineage, and auditing to support regulatory requirements.
• AI‑ready experience: Use AI-powered features to accelerate data discovery, SQL generation, and insight extraction from both structured and unstructured data.

With this foundation, firms can stand up AI and analytics use cases much faster, while simplifying architectures and reducing total cost of ownership.

Data & AI Business Use Cases Where Dremio Delivers Immediate Value

Below are five high‑impact financial services use cases, and how Dremio helps you execute them.

1. Real‑Time Fraud Detection & AML

Fraud and financial crime constantly evolve, so rules-based systems alone are no longer enough. AI models need fresh transactional, behavioral, and contextual data to spot subtle patterns in real time.

How Dremio helps:

• Combines streaming payments, card activity, login events, and historical customer data without forcing everything into a single transactional warehouse.
• Enables low‑latency SQL queries and feature generation directly on the data lake to feed fraud and AML models.
• Provides a governed semantic layer so data science, fraud operations, and compliance teams all work from consistent definitions of entities, thresholds, and risk scores.
• Supports AI-driven anomaly detection and AML scenarios such as structuring, mule account identification, and network analysis by making cross‑domain data easily accessible.

Result: Suspicious activity is identified sooner, false positives are reduced, and models adapt quickly as fraud patterns change without rebuilding the entire data stack.

2. Risk Aggregation & Regulatory Reporting

Regulations like Basel, FRTB, IFRS 9, and Solvency II demand accurate, timely, and auditable risk and finance reporting across fragmented systems. Traditional approaches, with complex overnight ETL chains and siloed data marts, are brittle and slow.

How Dremio helps:

• Unifies data from trading platforms, risk engines, finance systems, and reference data into a virtualized layer, reducing the need for physical consolidation.
• Provides a consistent semantic model for risk factors, exposures, capital measures, and hierarchies, so reports and dashboards align with regulatory definitions.
• Delivers fast, flexible analytics for tasks such as:
  • Intraday risk aggregation and VaR calculations.
  • Scenario and stress testing.
  • Regulatory disclosure preparation and drill‑downs.
• Maintains lineage and governance so teams can trace reported numbers back to source data, supporting internal model validation and external audits.

Result: Simpler data operations, faster close and reporting cycles, and greater confidence that AI and analytics outputs align with regulatory expectations.

3. Customer 360 & Hyper‑Personalization 

Winning and retaining customers now hinges on understanding them deeply and acting on that understanding in real time. That means building a true Customer 360 and powering AI models that can recommend the “Next Best Action” for each individual.

How Dremio helps:

• Connects CRM, digital channels, contact center logs, transactional history, product holdings, and third‑party data into a single, queryable view.
• Lets analytics and marketing teams create virtual “golden records” and customer segments directly on the lake, instead of pushing everything into bespoke customer data marts.
• Powers AI/ML workflows for:
  • Next Best Action and offer optimization.
  • Churn prediction and retention strategies.
  • Lifetime value and propensity modeling.
• Supports interactive analytics so product, marketing, and relationship managers can explore customer behavior and design campaigns or experiences without waiting on IT.

Result: Campaigns move beyond the generic to truly hyper‑personalized experiences across channels while keeping data management streamlined and controlled.

4. Portfolio, Performance, & Investment Analytics 

For buy‑side firms and wealth managers, performance and risk analytics must keep up with volatile markets and growing data complexity - from market and reference feeds to ESG, alternatives, and private assets.

How Dremio helps:

• Brings together positions, trades, benchmarks, risk factors, ESG scores, and external research data in one analytical environment.
• Enables portfolio managers, analysts, and quants to run:
  • Performance attribution and factor analysis.
  • Scenario and stress testing across portfolios and asset classes.
  • Exposure breakdowns by sector, region, ESG attributes, and more.
• Accelerates time‑to‑insight by allowing self‑service SQL and BI tools to query the lakehouse directly, with consistent metrics defined in the semantic layer.
• Makes it easier to experiment with new data sources and models, because you don’t need to redesign ETL and warehousing architecture each time.

Result: Faster, richer investment insight that supports alpha generation, better client reporting, and more responsive portfolio construction.

5. Insurance Analytics – Claims, Underwriting & Pricing

Insurers are transforming their businesses with AI-driven underwriting, claims automation, and dynamic pricing, but only if they can unify policy, claims, actuarial, and third‑party data at scale.

How Dremio helps:

• Provides a single environment to query policy systems, claims platforms, actuarial models, telematics/IoT feeds, and external risk data (e.g., weather, geospatial, credit, etc.).
• Supports underwriting and pricing models that:
  • Incorporate richer risk signals from devices, behavior, and environment.
  • Continuously recalibrate pricing based on emerging experience.
• Enables claims analytics for:
  • Fraud detection and anomaly spotting.
  • Severity and frequency forecasting.
  • Operational optimization across adjusters and service partners.
• Gives actuaries and data scientists a governed, performant workspace to iterate on models and assumptions without waiting for new data pipelines.

Result: More accurate pricing, faster and fairer claims, and a better understanding of portfolio risk driving both profitability and customer satisfaction.

Bringing It All Together

Across fraud, risk, customer intelligence, investments, and insurance, the pattern is the same: AI’s impact is gated by data. Dremio removes that bottleneck by giving financial institutions an intelligent lakehouse platform where all critical data is accessible, governed, and ready for analytics and AI—without the drag of legacy, copy‑heavy architectures.

For data, analytics, and business leaders in financial services, this means you can focus less on moving data and more on delivering new AI‑powered capabilities that actually move the needle.