Governed execution for regulated analytics
Designed to work in modern enterprtise stacks, trac is a governed execution layer that makes analytics auditable, repeatable and self-documenting.
Ideal for analytics that inform accounting, regulatory calculations and risl management
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One Framework. Multiple Uses
A governed execution layer for regulated analytics - trac's novel execution framework allows any Python analytics to run with built-in control, repeatability and flexibility.
For your most critical and highly governed analytics — including capital, liquidity and provisioning — trac ensures that every calculation is auditable, repeatable and self-documenting.
Combining the flexibility of a desktop tool with the control of an enterprise platform, TRAC allows you to recreate any prior run, vary assumptions and compare outcomes safely and quickly.
From ongoing model monitoring to independent validation, trac lets you manage libraries of statistical tests alongside model assets and run them safely against production models and data.
Immutable data management and on-demand recreation of historical transformations for lineage-heavy reporting, BCBS239 workflows and analytics assurance.
Across the model lifecycle
A solution for all stakeholders
A complete ModelOps solution for highly regulated analytics - trac provides modern tooling for all stakeholders across the three lines of defense
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MODEL DEVELOPER
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MODEL OWNER
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MODEL RISK MANAGER
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INTERNAL AUDITOR
With portable models and the flexibility to use production tools for model proving and analysis I can focus on what matters - building better models.
ExploreBuilt for self-service
Democratising model use
The unique control environment empowers model owners and model users to self-serve, free of change risk — manual controls and traditional access restrictions cease to be the bottleneck.
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Our philosophy
Built on open-source foundations
We publish trac's core analytic services as an open-source project (TRAC D.A.P.) via FINOS. The trac model runtime is also available to download from pypi.org.
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