Every system describing the same trade uses different field names, formats, and local conventions. That looks a lot like complexity. But it’s actually a translation problem, and translation has a well-established solution.
Why Trade Data Arrives in So Many Different Formats
Converting trade and risk data into a common standard can look difficult from the outside. Institutional market data feeds show the scale of the challenge: LSEG, for example, cites more than 9 million price updates per second, 500+ global exchanges, 1,000+ OTC markets and 90+ million instruments across its Real-Time Full Tick service.
Against that backdrop, a single transaction may appear as a proprietary booking message, a risk extract, a spreadsheet, an email, a term sheet, or a structured message such as FpML, FIX or ISO 20022. Each source has its own field names, formats and local conventions, which can create the impression that every system is describing something separate. In practice, these sources often describe the same underlying economic event for different operational purposes. The task is to translate the essential facts of the trade into a consistent, machine-readable form.
CDM: A Common Language for Post-Trade Data Normalisation
The FINOS Common Domain Model, or CDM, provides the common language for that translation. CDM gives firms a standard way to represent financial transactions, lifecycle events and related obligations. It starts from a practical observation: across asset classes and systems, a transaction involves counterparties, a product, a price, quantities, dates and contractual obligations. Much of the variation between systems sits in field names, message structures and local conventions. By mapping source data into CDM, firms can preserve the specific details of each trade while expressing it in a structure that downstream systems can understand.
How Tokenovate Converts Fragmented Inputs into a Structured Trade Record
Tokenovate’s data normalisation solution applies this model to post-trade operations. It acts as an ingestion and transformation layer that accepts data from emails, APIs and files, including CSV, FpML, FIX and ISO 20022, and converts those inputs into a structured CDM-standard record. The service then validates and enriches the data, creating a cleaner and more reliable foundation for matching, confirmation, reporting, lifecycle processing and settlement. Existing systems can remain in place while downstream workflows operate from a single authoritative trade record.
The Operational Cost of Post-Trade Data Fragmentation
This matters because post-trade fragmentation remains a major source of operational friction. A trade can pass through many systems across front, middle and back office functions, custodians, clearing houses and regulatory repositories. Each system may hold a version of the same trade, using different data structures, identifiers or levels of completeness. That creates reconciliation work, exception handling and delay.
The cost goes beyond just the operational. Gartner has estimated that poor data quality costs organisations at least $12.9 million per year on average, while the widely used 1-10-100 rule illustrates how errors become progressively more expensive the later they are found. Normalisation reduces this friction by binding downstream activity to a consistent representation of the original transaction, allowing each process to work from the same trade state.
Normalising Bespoke and Complex Trade Structures
Bespoke products can also be approached in a structured way. A tailored trade may contain specific legal or economic terms, but those terms can usually be decomposed into recognised components: parties, obligations, cashflows, dates, conditions and lifecycle events. CDM is designed to represent those building blocks while preserving product-specific detail. Tokenovate’s approach uses that structure to convert diverse trade inputs into a coherent record that can support automation across the post-trade lifecycle.
From Multiple Sources to One Consistent Trade State
The practical benefit is a more dependable operating model. Once a trade has been normalised, downstream workflows can draw from the same validated source rather than relying on repeated manual interpretation. Confirmation can reference the same economics as reporting. Settlement can trace back to the same lifecycle state as risk. Regulatory outputs can be generated from data that remains aligned with the authoritative trade record. This reduces duplicate checks, improves data quality and allows firms to process trades more efficiently across compressed settlement cycles and more automated market infrastructure.
What Normalised Post-Trade Data Makes Possible
The broader point is that data normalisation restores consistency to post-trade operations. Many systems hold the same trade facts in different formats, message styles and internal dialects. By using CDM as the common language and Tokenovate’s data normalisation as the ingestion layer, firms can turn fragmented trade and risk data into a unified source of truth that supports confirmation, reporting, lifecycle management and settlement with greater accuracy and less operational drag.
Ready to reduce post-trade fragmentation?
Speak to Tokenovate about turning fragmented trade and risk data into clean, CDM-standard records that support confirmation, reporting, lifecycle management and settlement from a single source of truth.