Data is the operational backbone of the financial services industry. Banks, insurers, trading platforms, and fintech organizations rely on data to manage risk, meet regulatory obligations, protect customers, and operate efficiently in volatile markets.
At the same time, financial data is among the most constrained data in any industry. It is distributed across regions, legal entities, business units, and systems. It is subject to strict regulatory oversight, data residency requirements, and security controls. These constraints are structural and intentional.
The challenge for financial institutions is not access to more data. It is how to generate insight and make decisions across fragmented data landscapes without increasing cost, risk, or regulatory exposure.
The operational reality: Why financial data remains siloed
Financial data silos are not a technical accident. They exist because of real operational constraints.
Core banking systems, trading platforms, risk engines, customer systems, and external data providers all operate under different ownership, latency, and compliance requirements. Centralizing this data introduces friction at every layer.
Institutions that attempt to solve this through centralized data lakes or warehouses face recurring problems:
- Rising infrastructure and storage costs driven by large scale data replication
- Increased security exposure due to aggregated sensitive datasets
- Regulatory risk caused by cross border or cross entity data movement
- Latency and data freshness issues that undermine time sensitive decisions
- Complex ETL pipelines that reduce agility and increase operational overhead
As data volume and velocity grow, these issues compound rather than disappear. Additionaly regulatory guidance such as BCBS 239 and GDPR increasingly emphasize controlled data processing, auditability, and locality over large scale data centralization.
Use case challenge: Analytics without centralization
Financial institutions need a way to analyze and operationalize data across silos without copying it into a single system.
They need to:
- Preserve data ownership and locality
- Enforce governance and auditability
- Support near real time and batch workloads
- Reduce cost and operational complexity
- Enable collaboration across organizational boundaries
This is the problem Scalytics Federated is designed to solve.
The Scalytics Federated approach: A Virtual Data Lakehouse
Scalytics Federated acts as a Virtual Data Lakehouse layer on top of existing financial data architectures.
Instead of moving data into a new centralized repository, Scalytics Federated executes analytics, queries, and machine learning workloads directly where the data already resides. This includes data warehouses, databases, object stores, operational systems, and partner environments.
The platform unifies these sources through federated execution, distributed query planning, and controlled model execution. Institutions gain a consistent analytical and decision making layer without creating a single physical data store.
This approach aligns with regulatory reality rather than working against it.
Proven impact in regulated financial environments
Based on industry deployments and analysis summarized in the Scalytics white paper Data Strategies in the Wake of AI, financial institutions applying federated data strategies have observed measurable benefits.
These include:
- Up to 35 percent reduction in data storage and replication costs by avoiding large scale central data duplication
- Faster time to insight by executing analytics directly on distributed data instead of relying on batch ingestion and ETL
- Reduced compliance and audit overhead by keeping sensitive data within its original system and jurisdiction
These outcomes are particularly relevant for institutions operating across regions and regulatory regimes, where data movement itself represents cost and risk.
Use Case 1: Federated Credit Scoring
The challenge
Credit scoring depends on combining signals from multiple internal and external data sources. These sources often span jurisdictions, organizations, and regulatory frameworks.
Credit bureaus, banking systems, and external data providers are frequently unable or unwilling to share raw data due to legal, contractual, or ethical constraints. Centralizing this data is often infeasible or creates unacceptable compliance risk.
As a result, credit decisions are made with incomplete or delayed information.
The federated solution
With Scalytics Federated, lenders execute credit scoring analytics across distributed datasets without transferring or exposing raw data.
Credit bureaus contribute insights without sharing individual credit reports. Banking systems provide behavioral signals without exposing transaction level data. External providers participate without relinquishing ownership or control.
Models and analytics are executed in place. Only governed results are aggregated.
Business impact
Financial institutions applying federated credit scoring benefit from:
- Broader risk signal coverage without violating data residency constraints
- Lower operational cost by eliminating redundant data pipelines
- Improved governance and auditability through controlled model execution
This enables more accurate and defensible credit decisions while remaining compliant with regulatory requirements.
Use Case 2: Federated Trading and Market Operations
The challenge
Modern trading platforms operate across multiple venues, counterparties, and custodians. Data quality, latency, and confidentiality are constant concerns.
Order data, transaction data, and settlement information are fragmented across systems with different trust and access boundaries. Centralizing this data introduces latency and increases exposure to operational and regulatory risk.
The federated solution
Scalytics Federated enables trading platforms to analyze and reconcile distributed trading data without central aggregation.
Transaction patterns can be analyzed across venues. Orders can be validated across systems. Custodian data remains protected while still contributing to operational insight.
Execution happens close to the data, preserving performance and confidentiality.
Business impact
Trading and market operations teams benefit from:
- Faster reconciliation and validation of distributed data
- Reduced dependency on centralized aggregation layers that introduce latency
- Improved collaboration across market participants without exposing proprietary data
This supports more resilient and efficient trading operations in complex market environments.
Why this matters for financial decision makers
Scalytics Federated does not attempt to replace existing financial data systems. It provides a pragmatic execution layer that works within real world constraints.
As a Virtual Data Lakehouse, it enables financial institutions to modernize analytics and decision making without compromising regulatory compliance, security, or operational control.
This approach aligns with how financial systems actually operate and how regulators expect them to behave.
Related capabilities
- Federated Intelligence for distributed analytics and data federation
- Streaming Intelligence for time sensitive risk and trading scenarios
- Private AI Platform for governed model execution in regulated environments
Industry context and regulatory references
The challenges described on this page reflect well documented realities in financial services data management and regulation.
They are consistently highlighted by regulators, standard bodies, and industry organizations, including:
- Bank for International Settlements (BIS) on data aggregation, risk management, and systemic resilience
https://www.bis.org - European Central Bank (ECB) guidance on data governance and risk data aggregation in financial institutions
https://www.ecb.europa.eu - Basel Committee on Banking Supervision (BCBS 239) principles for risk data aggregation and reporting
https://www.bis.org/bcbs/publ/d239.htm - General Data Protection Regulation (GDPR) requirements on data minimization, locality, and processing control
https://gdpr.eu - Financial Stability Board (FSB) on data fragmentation and cross-border financial systems
https://www.fsb.org - Scalytics white paper: Data Strategies in the Wake of AI
These frameworks reinforce the need for architectures that minimize unnecessary data movement while preserving governance, auditability, and control.