What Are the Primary Challenges in Sourcing RFQ Data for CAT Reporting?

Concept

Sourcing Request for Quote (RFQ) data for Consolidated Audit Trail (CAT) reporting presents a fundamental architectural conflict. The core of the issue resides in the collision of two distinct financial paradigms. On one side, you have the RFQ protocol, a mechanism rooted in bilateral, often bespoke, negotiation for sourcing liquidity, particularly for complex or large-scale trades. Its nature is conversational and context-dependent.

On the other side, there is the CAT reporting framework, an industrial-scale surveillance system built on the principles of absolute standardization, atomic data points, and unambiguous event sequencing. The primary challenge is the translation of nuanced, multi-stage negotiation workflows into the rigid, machine-readable syntax demanded by regulators.

The difficulty begins with the very definition of a reportable event within an RFQ lifecycle. The distinction between a non-immediately actionable (NIA) indication of interest and an actionable quote that constitutes a reportable “order” under Rule 613 is a significant point of friction. Many RFQ responses are part of a price discovery process and are not executable until further action is taken by the responding party. However, the CAT NMS Plan requires the reporting of all bids and offers communicated in a standard electronic format, creating a gray area that complicates compliance efforts.

This ambiguity forces firms to build complex logic to parse the intent and actionability of each electronic message, a task made more difficult by the diversity of RFQ platforms and communication protocols (e.g. FIX, proprietary APIs) used across the industry.

This translation is far from a simple data mapping exercise. It represents a deep operational and technological hurdle. Firms must capture a fragmented and varied set of data points from disparate systems, including Order Management Systems (OMS), Execution Management Systems (EMS), and the RFQ platforms themselves. These systems were often designed for trading efficiency, not for the granular audit trail requirements of CAT.

Consequently, the necessary data ▴ such as precise timestamps for every stage of the RFQ, unique identifiers for all parties, and the full content of each quote response, selected or not ▴ is often scattered, inconsistent, or altogether missing. The challenge is therefore one of system integration, data remediation, and the creation of a coherent, auditable narrative from a series of disconnected electronic conversations.

Strategy

A robust strategy for managing RFQ data for CAT reporting is built upon a foundational principle of creating a single, coherent data architecture. The goal is to bridge the gap between the unstructured nature of RFQ negotiations and the highly structured demands of CAT. This requires a multi-pronged approach that addresses data capture, workflow interpretation, and technological integration. Firms must decide whether to build an in-house system, leverage a third-party vendor solution, or implement a hybrid model that combines internal expertise with specialized external technology.

A successful CAT reporting strategy transforms compliance from a reactive data-gathering exercise into a proactive, architecturally sound process.

Architectural Approaches to RFQ Data Aggregation

The choice of architectural model is a critical strategic decision with long-term implications for cost, control, and compliance resilience. Each approach presents a different set of trade-offs that must be aligned with the firm’s operational capabilities and risk appetite.

• In-House Development This strategy involves building a proprietary system to aggregate RFQ data from all sources, normalize it, apply the necessary CAT reporting logic, and generate the final submission files. This provides maximum control over the data and logic but requires significant and ongoing investment in development, maintenance, and expertise to keep pace with evolving CAT requirements.
• Vendor-Based Solutions Engaging a specialized RegTech vendor can accelerate implementation and reduce the internal development burden. These vendors offer dedicated CAT reporting platforms that are pre-configured to handle various data sources, including RFQ platforms. The primary trade-off is a potential reduction in control and the need to rely on the vendor’s interpretation of complex reporting rules.
• Hybrid Model This approach combines the strengths of the other two. A firm might use a vendor solution for the heavy lifting of data ingestion and formatting while retaining an in-house team of subject matter experts to manage data quality, oversee the application of reporting logic, and handle exceptions and error remediation. This balanced strategy often provides a good mix of efficiency and control.

The table below compares these strategic approaches across key decision-making criteria, offering a framework for evaluating the optimal path for a given institution.

How Does Data Fragmentation Impede Reporting?

A core strategic challenge is overcoming data fragmentation. RFQ data resides in multiple systems that are not natively designed to communicate with each other for reporting purposes. An RFQ may be initiated in an EMS, with responses received via a third-party platform or directly into an OMS through FIX messages.

Each system captures a piece of the overall event, creating data silos. Without a unified data strategy, firms are forced into inefficient and error-prone manual processes to stitch together the required information for a single RFQ lifecycle.

The strategic solution is the establishment of a “golden source” for CAT-related data. This involves creating a central repository or a data lake architecture where all relevant events from the OMS, EMS, and RFQ platforms are ingested and time-synchronized. This centralized data source then becomes the foundation for the CAT reporting engine, which can apply consistent logic and enrichment rules. This strategy directly addresses the lack of standardization and integration challenges highlighted by industry analyses.

Execution

The execution of a CAT reporting framework for RFQ workflows is an exercise in precision engineering. It demands a granular understanding of the data elements, a meticulously designed workflow for data transformation, and a resilient technological architecture to ensure completeness, accuracy, and timeliness. The process must be auditable at every step, from the initial sourcing of a quote to the final acceptance of the reported data by the FINRA CAT system.

Effective execution hinges on the ability to translate every step of a complex negotiation into a discrete, reportable event with verifiable data.

The RFQ-To-CAT Reporting Lifecycle

Executing the reporting process requires a systematic, multi-stage approach. Each stage addresses a specific part of the data journey, ensuring that raw, fragmented information is progressively refined into a compliant submission.

1. Data Ingestion and Synchronization The initial step is to capture all relevant messages and events from the source systems. This includes the RFQ initiation from the EMS, all responses (both selected and unselected) from the RFQ platform or FIX gateway, and the final execution or order placement in the OMS. Critically, all timestamps must be synchronized to a common clock source (e.g. NIST) and converted to Coordinated Universal Time (UTC) as required by CAT.
2. Event Interpretation and Sequencing Once ingested, the raw data must be interpreted to identify the specific reportable CAT events. This is where the logic to differentiate between a non-actionable indication and a reportable quote is applied. The system must reconstruct the entire lifecycle of the RFQ, linking each response back to the initial solicitation to create a complete audit trail.
3. Data Enrichment and Validation The interpreted events are then enriched with required CAT data fields that may not be present in the source systems. This includes assigning a unique firmDesignatedId to the RFQ lifecycle, mapping internal trader identifiers to CAT-required IDs, and adding context-specific details. The enriched data must be validated against CAT’s technical specifications to catch formatting errors or missing information before submission.
4. Submission and Error Reconciliation The validated and formatted data is compiled into files and submitted to the FINRA CAT portal. The execution process does not end here. The firm must have a robust mechanism to ingest feedback files from CAT, identify any rejections or linkage errors, and manage a timely and accurate correction and resubmission process. This reconciliation loop is a critical component of ongoing compliance.

What Data Must Be Captured and Transformed?

The core of the execution challenge lies in mapping the disparate data elements from RFQ systems to the precise fields required by the CAT technical specifications. The temporary exemption for Non-Immediately Actionable (NIA) Electronic RFQ Responses until July 31, 2026, gives firms time to build these systems, but the underlying data requirements remain. The table below provides a simplified example of this mapping for a reportable RFQ response, illustrating the transformation required.

This mapping process highlights the need for a sophisticated data transformation engine. The system must handle variations in data formats from different RFQ sources, apply complex business rules to determine reportability, and enrich the data with external information like regulatory identifiers. The complexity multiplies for multi-leg option strategies, where each leg of the RFQ must be reported with appropriate linkages, a phase of reporting that presents its own significant challenges.

Reflection

The architectural undertakings required to meet CAT reporting obligations for RFQ workflows compel a deeper examination of a firm’s entire data infrastructure. The process of sourcing, translating, and reporting this specific data stream reveals the true state of system integration and data governance within an organization. Viewing this challenge through the lens of regulatory compliance alone is insufficient. Instead, it should be seen as a catalyst for building a more resilient, transparent, and efficient operational framework.

The systems architected to solve this problem can yield benefits far beyond compliance, providing clearer insights into execution quality, counterparty performance, and liquidity sourcing. The ultimate question for any institution is how it can transform the mandated transparency of CAT into a strategic asset for superior market navigation.