Secure, compliant Federated AI for Finance

Financial AI Challenges

In finance, AI is only deployable when you can prove control: where data was processed, which policies applied, how outputs were produced, and how changes are governed. Most programs fail when they depend on centralizing sensitive data, creating new copies, and stitching together un-auditable pipelines.

Auditability and Model Governance
Finance needs defensible AI: traceable inputs, controlled execution, and evidence-ready outputs for internal risk functions and external regulators.
Traceability across data, code, and execution
Controlled change and deployment pathways
Evidence for audits and operational resilience
Data Gravity and Legacy Estates
Core banking systems, market data stacks, and regulated stores cannot be refactored on a vendor timeline. AI must integrate without forced migration.
Integration across heterogeneous platforms
Avoid new copies of sensitive datasets
Modernization without disruptive rewrites
Operationalization Under Control
The hard part is not the model. It is repeatable, governed execution in production across teams, environments, and security boundaries.
Standardized patterns instead of one-off pipelines
Controlled deployment across on-prem and cloud
Operational resilience without platform sprawl
Built on an open, proven foundation

Scalytics builds on Apache Wayang, the cross-platform data processing framework created by our founding team and now an Apache Top-Level Project. Instead of moving data into centralized infrastructure, Scalytics brings compute to your data so you can deploy AI and analytics across distributed sources without violating compliance boundaries.

Scalytics Federated: Privacy-First Architecture Built for Financial Systems

Designed for regulated environments where data locality, auditability, and control are non-negotiable

Risk Management

Deploy real-time risk analytics and portfolio controls across distributed data sources without violating regulatory or data residency constraints.

Fraud Detection

Run advanced fraud detection on transactional and event streams using private AI models without exposing sensitive customer data.

Credit Scoring

Develop credit scoring models on sensitive financial data with full governance, explainability, and controlled model execution.

Seamless Integration

Modernize without breaking controls. Connect to your existing estate and operationalize analytics without extracting sensitive data into new platforms.

Connect Legacy Systems

Integrate core systems and regulated stores without disruptive migration

Transform Data

Prepare features and datasets where they live, under existing controls

Enable AI Models

Train and run models within your security perimeter, not outside it

Operationalize Outcomes

Deliver insights and actions with traceable execution and defensible results

Deploy governed AI across distributed data sources without forced centralization. Keep auditability, data control, and operational resilience from prototype to production.

Operational Efficiency

Efficiency in finance is control at scale: standard patterns, governed deployments, and less platform sprawl. Reduce operational load while increasing audit readiness.

Reduce Platform Toil
Consolidate execution patterns across engines and teams, with a consistent operational model across environments.
Strengthen Governance
Make execution traceable and repeatable so risk, compliance, and engineering can operate from the same evidence.
Ship Controlled Change
Shorten the path from prototype to production without bypassing change control, auditability, or security boundaries.

Flexible Deployment Options

Choose the deployment model that fits your security perimeter, operational resilience requirements, and internal governance.

On-Premise
Full control within your existing security boundary for sensitive workloads
Hybrid
Keep sensitive execution inside, scale selectively, and govern both sides consistently
Cloud-Native
Modern delivery and elasticity with strong controls for regulated environments

Financial Services Use Cases

Deploy AI where the data lives, keep control, and operationalize outcomes with defensible execution in regulated environments.

Risk Management
Real-time risk analytics and portfolio optimization without breaking data control or governance boundaries
Fraud Detection
Private model execution for fraud detection while limiting exposure and reducing uncontrolled replication
Investment Analysis
Sophisticated analysis with governed pipelines that remain traceable across data sources and engines
Customer Service
Assistive AI that stays within your perimeter, with controlled access and predictable operational behavior
Regulatory Reporting
Automate reporting with defensible outputs designed for accuracy, traceability, and audit readiness
Credit Scoring
Develop scoring models across sensitive datasets without forcing centralization or weakening control

A Federated Data Lakehouse for Banking and Financial Services

Scalytics Federated enables secure, federated data execution across distributed sources without compromising privacy.
A Federated Data Lakehouse for Banking and Financial Services
Read more

Financial Data Signaling and Decision Acceleration

Scalytics Federated enables banking and financial services organizations to treat data as a strategic asset for timely, reliable insights.
Financial Data Signaling and Decision Acceleration
Read more

Stop Centralizing Sensitive Financial Data to “Enable AI”

Centralization creates new copies, new attack surfaces, and governance gaps that are expensive to defend and harder to explain in audits. Scalytics brings compute to your data so you can deploy AI across distributed sources without violating compliance boundaries, changing your security posture, or betting on a risky migration program.

If you are evaluating AI for regulated workflows, we will walk through your architecture and show where centralization typically breaks: data control, traceability, and operational resilience.

About Scalytics

Scalytics builds on Apache Wayang, the cross-platform data processing framework created by our founding team and now an Apache Top-Level Project. Where traditional platforms require moving data to centralized infrastructure, Scalytics brings compute to your data—enabling AI and analytics across distributed sources without violating compliance boundaries.

Scalytics Federated provides federated data processing across Spark, Flink, PostgreSQL, and cloud-native engines through a single abstraction layer. Our cost-based optimizer selects the right engine for each operation, reducing processing time while eliminating vendor lock-in.

Scalytics Copilot extends this foundation with private AI deployment: running LLMs, RAG pipelines, and ML workloads entirely within your security perimeter. Data stays where it lives. Models train where data resides. No extraction, no exposure, no third-party API dependencies.

For organizations in healthcare, finance, and government, this architecture isn't optional, it's how you deploy AI while remaining compliant with HIPAA, GDPR, and DORA.Explore our open-source foundation: Scalytics Community Edition

Questions? Reach us on Slack or schedule a conversation.

Scalytics Copilot:
Real-time intelligence. No data leaks.

Launch your data + AI transformation.

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