How Has the Rise of Systematic Internalisers Complicated the EMS and OMS Relationship?

Concept

The introduction of the Systematic Internaliser (SI) regime under MiFID II represented a fundamental re-architecting of the European trading landscape. It was conceived to enhance transparency in what was previously opaque, off-exchange bilateral trading. An SI is an investment firm that deals on its own account by executing client orders outside a regulated market or multilateral trading facility (MTF) on an organized, frequent, and systematic basis.

This created a new, formal category of liquidity venue, one that operates under a unique set of rules distinct from traditional exchanges and dark pools. For the buy-side, this was not merely an additional destination for order flow; it was a systemic intervention that directly challenged the established logic and data pathways connecting the two most critical components of their trading infrastructure ▴ the Order Management System (OMS) and the Execution Management System (EMS).

Historically, the relationship between the OMS and EMS was linear and well-defined. The OMS, the system of record, managed the lifecycle of an order from portfolio-level decision to pre-trade compliance and allocation. It was the source of truth for the firm’s positions and intentions. The EMS was the tactical layer, the cockpit for the trader responsible for working that order in the market.

It received the order from the OMS and provided the tools for liquidity discovery, algorithmic execution, and real-time market data analysis. The flow was unidirectional ▴ from strategic intent (OMS) to tactical execution (EMS). The rise of SIs fractured this clean workflow. SIs introduced a source of principal liquidity that operated on a bilateral, quote-driven basis, a stark contrast to the anonymous, continuous order books of lit markets or the passive matching of dark pools.

This new execution channel could not be treated as just another venue in the EMS dropdown menu. Interacting with an SI required a different mode of engagement, one that involved direct, identifiable communication and a new set of data considerations that blurred the lines between the strategic and tactical layers of the trading stack.

The core complication introduced by Systematic Internalisers is the disruption of the traditional, linear data flow between the strategic Order Management System and the tactical Execution Management System, forcing a move towards a more integrated and dynamic architecture.

The Old World a Linear Conveyor Belt

Before the widespread adoption of the SI regime, the OMS-to-EMS workflow functioned like a well-oiled assembly line. A portfolio manager’s decision would generate a parent order in the OMS. This system would handle all the pre-trade checks ▴ Is the order compliant with client mandates? Does it fit within the fund’s risk limits?

Once cleared, the order was sliced into child orders and electronically passed to the EMS. At this point, the OMS’s primary role was to wait for execution reports to flow back from the EMS for position updates and post-trade allocation. The trader, operating within the EMS, viewed the market through the lens of available lit and dark venues, using algorithms and smart order routers (SORs) to navigate this landscape. The EMS was the window to the external market, while the OMS was the internal ledger.

The data exchange was standardized, primarily consisting of order details, execution fills, and status updates, often communicated via the Financial Information eXchange (FIX) protocol. This separation of duties created a clear boundary; the OMS managed the ‘what’ and ‘why’ of the trade, and the EMS handled the ‘how’ and ‘when’.

The SI Disruption a Networked Reality

Systematic Internalisers shattered this linear model by introducing a hybrid liquidity source that demanded a more complex, networked approach. SIs are not anonymous pools; they are counterparties. A firm must onboard an SI operator, establishing a direct legal and technical relationship. This is fundamentally an OMS-level concern, involving counterparty risk management and legal agreements.

Yet, the interaction with an SI is a real-time execution decision, traditionally the domain of the EMS. An SI provides quotes, often in response to an indication of interest (IOI) or a direct request for quote (RFQ). This quote stream is a new, vital source of pre-trade data that needs to be captured, normalized, and integrated directly into the trader’s decision-making process within the EMS. Suddenly, the clean separation of duties dissolved.

The EMS needed to be aware of OMS-level counterparty relationships to know which SIs were available. The OMS, in turn, needed to process and understand execution data that came not from a public venue but from a specific, bilateral counterparty, complicating post-trade analysis and best execution reporting. The conveyor belt had been replaced by a complex web of interconnected nodes, each requiring data and context from the other to function effectively.

Strategy

The emergence of Systematic Internalisers requires a strategic overhaul of the entire trading workflow, moving beyond simple technological adaptation to a fundamental rethinking of how liquidity is sourced, evaluated, and executed. The once-clear demarcation between the OMS and EMS has become a permeable membrane, demanding a new strategic framework centered on integration, data enrichment, and dynamic feedback loops. Firms that continue to operate with a siloed OMS/EMS mentality will find themselves unable to effectively access SI liquidity or, more critically, prove best execution in a fragmented market.

Re-Architecting Liquidity Discovery

The primary strategic challenge is the integration of SI quote streams into the liquidity discovery process. Unlike lit markets, where the EMS passively consumes a public data feed, SI liquidity is actively solicited. This necessitates a multi-stage strategic response.

• Counterparty Management as a Pre-Trade Function ▴ The OMS must evolve from a static repository of legal agreements into a dynamic source of truth for available SI counterparties. This information must be piped to the EMS in real-time, allowing the EMS to know which SIs to query for a specific instrument.
• IOI and RFQ Integration ▴ The EMS must be equipped with the native functionality to handle indication of interest (IOI) and request for quote (RFQ) workflows. This is a departure from simply routing an order to a venue. It involves sending a message to a specific set of SIs, receiving multiple quotes back, and presenting them to the trader in a coherent, actionable format. The logic for which SIs to query for which type of order becomes a critical part of the execution strategy.
• Smart Order Router (SOR) Evolution ▴ A traditional SOR is designed to find the best price across lit and dark order books. An SI-aware SOR must be far more sophisticated. It needs to be able to intelligently decide when to access the public markets versus when to solicit quotes from SIs. This decision is based on a host of factors including order size, market impact sensitivity, instrument liquidity, and the historical performance of specific SIs.

Adapting to the SI regime requires transforming the smart order router from a price-seeking tool into a strategy engine that dynamically chooses between anonymous order books and bilateral quote solicitations.

The Data Fusion Imperative

A successful SI strategy hinges on the ability to fuse data from the OMS and EMS into a single, coherent view for both pre-trade decision-making and post-trade analysis. The clean hand-off of an order is no longer sufficient; a continuous, bi-directional data dialogue is required.

This new data architecture has profound implications for Transaction Cost Analysis (TCA). Traditional TCA benchmarks often rely on public market data (e.g. VWAP, arrival price). Evaluating an SI execution requires a different set of metrics.

The quality of an SI fill must be compared not only to the public market at the time of the trade but also to the other SI quotes that were received but not taken. This creates a need for the EMS to capture all quote data, not just the winning one, and feed it back into a centralized analytics engine that can also access the strategic context from the OMS. The table below illustrates the evolution of data requirements.

Table 1 ▴ Evolution of OMS/EMS Data Exchange

Execution

The execution framework in an SI-inclusive world is one of heightened complexity, demanding specific technological capabilities and refined operational protocols. The theoretical strategies of data fusion and integrated workflows must be translated into tangible system configurations and trader actions. This requires a granular focus on the communication protocols, the logic of the execution algorithms, and the architecture of the post-trade analytics that validate the entire process.

The FIX Protocol as the Lingua Franca

The Financial Information eXchange (FIX) protocol remains the central nervous system for order and execution data, but its implementation must be adapted for SI interaction. Standard order routing messages are insufficient. The execution layer must be fluent in the specific FIX message types that govern RFQ workflows. A typical workflow might proceed as follows:

1. Quote Request (FIX MsgType=R) ▴ The trader, through the EMS, initiates an RFQ for a specific instrument. The EMS, referencing the live counterparty data from the OMS, sends a Quote Request message to a selected list of SI counterparties. This message contains a unique QuoteReqID, the instrument details, and the desired quantity.
2. Quote Response (FIX MsgType=S) ▴ The SIs respond with Quote messages. Crucially, the EMS must be able to handle multiple, asynchronous Quote messages, each linked to the original QuoteReqID. Each message contains the SI’s firm bid and offer, their associated sizes, and a QuoteID for that specific quote.
3. Order Execution (FIX MsgType=D/F/G) ▴ If the trader decides to accept a quote, the EMS sends a New Order Single (or similar order type) message to the chosen SI, referencing the specific QuoteID they wish to hit. This creates a firm, executable order linked directly to the previously supplied quote.

The EMS must parse and display these competing quotes in a clear, consolidated “RFQ ladder,” allowing the trader to make an informed, one-click execution decision. The system must also manage the lifecycle of these quotes, which are typically only valid for a very short period.

Advanced Smart Order Routing Logic

An SI-aware Smart Order Router (SOR) is the brain of the modern execution workflow. Its logic must extend far beyond simple price-based routing. It functions as a decision engine, determining the optimal path for an order based on a matrix of variables. The configuration of this SOR is a critical execution task.

Table 2 ▴ SOR Decision Matrix Example

Effective execution in the modern trading environment is defined by the sophistication of the smart order router’s logic, which must weigh the certainty of a bilateral quote against the anonymity of a central limit order book.

High-Fidelity Transaction Cost Analysis (TCA)

Finally, the execution process is incomplete without a robust TCA framework that can validate the choices made. Best execution is no longer about getting the best price on a single venue; it’s about proving that the chosen execution method was the best possible strategy given all available information at the time. This requires the capture and analysis of data that was previously discarded.

• Quote Log Analysis ▴ The system must log every quote received from every SI, not just the one that was executed. Post-trade analysis should compare the executed price against all quotes received, demonstrating why the winning quote was selected (e.g. best price, largest size).
• Market Impact Benchmarking ▴ For SI trades, the analysis must calculate the “price improvement” versus the prevailing public market bid/offer (the EBBO – European Best Bid and Offer) at the moment of execution. This demonstrates the value added by accessing SI liquidity.
• Information Leakage Measurement ▴ A more advanced analysis will attempt to measure information leakage. Did initiating an RFQ with a group of SIs cause a price movement in the public markets before the trade was executed? This involves analyzing market data immediately following the Quote Request message and is a critical factor in refining the list of trusted SI counterparties.

This level of analysis requires a tight integration between the EMS, which captures the execution and quote data, and a powerful data analytics platform that can store, process, and visualize these complex datasets. The output of this TCA process feeds directly back into the pre-trade strategy, creating a continuous loop of improvement where execution decisions are constantly refined based on empirical performance data.

Reflection

From Linear Process to Dynamic System

The integration of Systematic Internalisers has irrevocably transformed the trading desk’s operational model from a linear, sequential process into a dynamic, interconnected system. The core challenge is not merely technological ▴ it is philosophical. It requires firms to view their OMS and EMS not as separate tools performing discrete tasks, but as a single, cohesive execution platform. The data, logic, and workflows must be interwoven to create a system where strategic intent continuously informs tactical execution, and tactical feedback constantly refines strategic decision-making.

Considering this new reality, the critical question for any trading desk is whether its current infrastructure is built on the logic of the past or architected for the demands of the present. Is the flow of information between your systems a one-way street or a multi-lane highway? How is counterparty data from the OMS leveraged in real-time by the execution logic in the EMS?

And how does the rich data captured by the EMS ▴ the full spectrum of quotes, not just the executed trades ▴ feed back to inform the firm’s overarching strategy? Answering these questions reveals the true readiness of an operational framework to compete in a market where the edge is found in the seamless fusion of all available information.