Investment firms run on data, but that data is typically scattered across dozens of systems, formats, and vendors. Reconciling it absorbs disproportionate effort. Trusting the result is harder than it should be. The majority of technology spend goes to maintaining existing systems, leaving little room for innovation or growth.
- Who we serve
Who we serve
- Solutions
Solutions
- Company
Enterprise Data Management (EDM+)
A data foundation that knows what the data is for. Reference, market and entity data mastered in the same store that holds your positions.
The Industry Challenge
How FINBOURNE helps
Your systems stay where they are. EDM+ works with what you already run. Each source system keeps its own model and its own identifiers inside its own scope, mastered without being forced into a single shape. Aladdin, SimCorp, Geneva, Multifonds, your administrator’s files: they can stay where they are. EDM+ becomes the layer that agrees on what is true, and every value stays traceable to the source it came from.
Seamless integration with any data source
Connect to any system or provider through 850+ APIs. Use a flexible, entitlement-aware data model with built-in multi-tenancy that evolves with your business. Add new instruments and asset classes without the months of development and testing that new integrations normally require.
Unified, trusted data foundation
Access a single, virtualised dataset. Pull, master, view, and transform live data from any source without moving or duplicating it. Automate reconciliation across your entire data universe.
Instant access to complete data histories
Replay any data point on any past date, exactly as it was understood then, and again as it is understood now. Every value carries both the date it applied and the date you learned it, so a price restated last week never overwrites what your team saw at the time.
The data layer your AI agents need
Your data foundation is also your AI foundation. FINBOURNE's financial operating layer gives AI agents the domain context, entitlements, and audit trail they need to reason over live investment data, not stale snapshots.
ACCESS LAYER
Your team
Your AI
Your systems
Connect
Brokers Custodians Trade execution data Third party apps Data stores Fund admins Public & private market dataVIRTUALIZE
Governed, automated risk data delivery for a major pension fund
Trust
DATA QUALITY
Your data: accurate, complete, reliable
MASTER
Resolves conflicts across systems into one trusted record
OPERATIONAL DATA STORE
Full history, as-of any point in time, for audit & restatement
Act
OMS PMS IBOR Compliance Fund accounting Transfer agency
+ more
Award-winning excellence
Best AI-Based Solution for Data Management 2026
Most Innovative Quality Initiative 2026
Best Data Management Solution 2026
Best Enterprise Data Management Initiative 2026
Best Investment Operations & Data Platform 2026
Best Data Management Solution 2025
Best Buy-Side Data Management Solution 2025Features
01Data virtualisation and analytics
Query across your entire data universe without moving or copying data. A single access point that exposes data from any underlying source, allowing it to be queried, joined, and transformed on the fly. Power dashboards, reports, BI tools, and AI agents from a single governed layer.
02Data quality
Configurable validation rules with automated exception detection. Data quality scores provide confidence metrics across your entire data universe. Exception workflows route issues to the right team with full context.
03Data mastering
Instruments and securities, prices and time series, corporate actions, credit ratings, ESG, legal entities and counterparties, funds and portfolios, and client-defined datasets. The authoritative view is resolved on query from all contributing sources, not a persisted copy.
04Data governance and lineage
The rules, ownership and accountability for your data. Define who is responsible for each dataset, what quality standards apply, and what entitlements govern access. Data lineage is the evidential counterpart: the factual record of where data came from, what transformed it, and where it went.
05Reconciliation
Repeatable reconciliations across any combination of internal systems, custodians, administrators, and counterparties. Configurable matching rules. Exception-based dashboards surface breaks instantly.
06Data matching
Flexible mapping and matching rules to align data from multiple sources, different identifiers, naming conventions, and taxonomies, into a trusted, unified dataset. Cross-referencing across multiple vendor codes with configurable priority hierarchies.
AI
AI in financial services needs governed data to work. With FINBOURNE, there are no RAG pipelines, no text-to-SQL against raw databases. EDM+ sits between your AI agents and your data, giving them live, governed access to financial concepts rather than tables, with entitlements enforced and every call auditable as at any point in time. Connect over MCP and use the AI tools you already use.
Fund accounting agent Transfer agency agent Private equity agent + moreDavid Pierson: AI and the financial operating layer
Ready to see FINBOURNE in action?
Questions we hear from investment data teams
How are other firms reducing the time spent reconciling data across multiple systems?
By virtualising data from each source system into a single access layer rather than copying it into a central store. Reconciliation rules run across that layer on schedule, with exceptions routed to the right team automatically.
What does a single view of instrument and pricing data look like when it comes from multiple vendors?
Each vendor’s data is ingested and mastered under its own scope, with configurable priority rules determining which source wins for each attribute. The result is one authoritative view resolved on query, not a persisted copy that goes stale.
How do I add a new asset class to my data platform without rebuilding integrations?
FINBOURNE’s data model is extensible by configuration. A new asset class, instrument type, or data source is added through the same API catalogue that serves the existing ones, without development work on the integration layer.
What does investment data need to look like before AI agents can work with it?
Agents need data organised by financial concept (instruments, positions, fund hierarchies, corporate actions), not by database table. FINBOURNE’s financial operating layer provides that, with entitlements enforced and every agent call auditable via MCP.
How are firms giving different teams access to the same data without maintaining separate copies?
Through entitlement-controlled views on a single dataset. Each team sees the data relevant to their function, governed by role-based permissions, without anyone maintaining a separate extract or copy.
- Solutions
