What Specific Data Points Are Required to Evidence Compliance with Both MiFID II Best Execution and the Consumer Duty?

Concept

From Mechanistic Compliance to Holistic Value

The convergence of the Markets in Financial Instruments Directive II (MiFID II) best execution framework and the Financial Conduct Authority’s (FCA) Consumer Duty represents a fundamental recalibration of regulatory expectation. It moves the operational focus from a set of discrete, transaction-based assessments to a continuous, evidence-based demonstration of client value. The central challenge for any financial institution is the construction of a data architecture that can satisfy both the granular, quantitative demands of MiFID II and the broader, qualitative outcomes mandated by the Consumer Duty. This is not a matter of running two parallel compliance streams; it is about designing a single, coherent system that translates execution data into a narrative of fair value.

MiFID II, in its essence, provides the foundational syntax for execution quality. It compels firms to take all sufficient steps to obtain the best possible result for their clients, considering factors like price, costs, speed, and likelihood of execution. The regulation demands a rigorous, data-driven approach to monitoring and evidencing these steps. The now-suspended reporting obligations under RTS 27 and RTS 28, while no longer a formal requirement in the UK and subject to deprioritization in the EU, established a blueprint for the types of data points firms must collect and analyze.

These include intricate details about execution venues, prices achieved against benchmarks, and the explicit and implicit costs associated with a transaction. This framework is mechanistic, precise, and focused on the moment of execution.

The Consumer Duty introduces a supervening principle. It requires firms to act to deliver good outcomes for retail customers, a mandate that extends far beyond the transaction itself. This principle is substantiated by four key outcomes ▴ Products and Services, Price and Value, Consumer Understanding, and Consumer Support. The “Price and Value” outcome directly intersects with MiFID II’s best execution requirements.

A firm cannot demonstrate fair value without first demonstrating best execution. However, best execution alone is insufficient to prove fair value. The Duty compels a firm to consider the total cost of a product or service relative to the overall benefits a consumer receives, a calculation that includes not just transaction costs but management fees, platform charges, and even non-monetary costs like the use of personal data.

A unified compliance framework must treat MiFID II’s execution data as the critical input for the Consumer Duty’s broader fair value assessment.

Therefore, the data required to evidence compliance is multi-layered. The first layer consists of the high-frequency, granular data points related to trade execution, as prescribed by the spirit of MiFID II. The second, more complex layer involves contextual data that frames the transaction within the client’s overall relationship with the firm.

This includes information about the client’s objectives, their characteristics of vulnerability, the suitability of the product, and the clarity of communications. The challenge is to build a system that can ingest, link, and analyze these disparate data sets to produce a single, defensible conclusion ▴ that the outcome delivered was not only efficiently executed but also represents fair value for that specific client in their specific circumstances.

Strategy

The Integrated Data Governance Framework

A strategic approach to satisfying both MiFID II and the Consumer Duty requires moving beyond siloed compliance functions and establishing an integrated data governance framework. This framework’s primary objective is to create a single, longitudinal view of the client, where execution data is a critical component of a much larger narrative about value. The strategy is not merely to collect data points, but to architect a system that connects them, revealing the causal links between a firm’s actions and client outcomes.

The core of this strategy involves mapping the entire client and trade lifecycle and identifying data capture points at every stage. This process begins long before an order is placed and continues well after it has settled. It necessitates a fundamental shift from viewing compliance as a series of discrete checks to seeing it as a continuous monitoring process powered by interconnected data streams.

From Execution Factors to a Value Chain Analysis

MiFID II forces a focus on the “execution factors” ▴ price, costs, speed, likelihood of execution and settlement, size, and nature of the order. The Consumer Duty compels firms to place these factors within a broader “value chain.” A successful data strategy must therefore be able to quantify not only the quality of execution but also its contribution to the overall value proposition for the client. This means linking execution data from Order Management Systems (OMS) and Execution Management Systems (EMS) with client data from Customer Relationship Management (CRM) systems, fee and commission engines, and even unstructured data from complaints logs and client communications.

The following table illustrates the strategic shift in focus from a pure MiFID II perspective to an integrated MiFID II and Consumer Duty perspective:

Bridging Quantitative Metrics with Qualitative Evidence

A significant strategic challenge is the integration of quantitative execution metrics with the qualitative evidence required by the Consumer Duty. While MiFID II compliance can be largely evidenced through Transaction Cost Analysis (TCA) and other numerical reports, the Consumer Duty requires firms to evidence their considerations of factors like client understanding and foreseeable harm.

Effective data strategy translates quantitative execution metrics into tangible evidence of good or poor client outcomes.

This involves developing new, composite metrics. For instance, a firm might create a “Value Score” for a particular product or service. This score would incorporate:

• Quantitative Inputs ▴ Average execution slippage, total expense ratio (TER), and performance against a benchmark.
• Qualitative Inputs ▴ Scores from client understanding surveys, complexity ratings for product literature, data on complaint frequency, and flags for clients with characteristics of vulnerability.

Such a strategy requires a robust data infrastructure capable of handling both structured and unstructured data, and an analytics layer that can perform the necessary correlations. The ultimate goal is to equip the firm’s governance bodies with a holistic dashboard that moves beyond simple execution statistics to provide a clear, evidence-based assessment of whether fair value is being delivered to all client segments.

Execution

The execution of a unified compliance framework for MiFID II and the Consumer Duty is a complex undertaking in system and data architecture. It requires a granular, multi-stage approach that transforms regulatory principles into operational reality. This is not a simple reporting exercise; it is the construction of a firm-wide data nervous system designed to monitor, analyze, and evidence client outcomes in near real-time.

The Operational Playbook

Implementing a robust data framework requires a clear, sequential plan. This playbook outlines the critical steps from data identification to governance and reporting.

1. Data Source Identification and Lineage Mapping ▴ The initial step is a comprehensive audit of all systems that hold relevant data. This involves identifying the “golden source” for each data point and mapping its lineage from creation to storage. Key systems include:
   • Order & Execution Management Systems (OMS/EMS) ▴ The source for all pre-trade, at-trade, and post-trade data points, including timestamps, order types, venue selection, and execution prices.
   • Customer Relationship Management (CRM) Systems ▴ Holds client-specific data, including target market segmentation, communication history, contact details, and crucially, any identified characteristics of vulnerability.
   • Fee and Commission Engines ▴ The source for all explicit costs charged to the client, including advisory fees, platform fees, and custody charges.
   • Complaints Management Systems ▴ A critical source of qualitative data on poor outcomes, client confusion, or dissatisfaction.
   • Product Master Files ▴ Contains the features, objectives, and intended target market for every product offered.
2. Data Ingestion and Centralization ▴ Once sources are identified, the firm must establish robust data pipelines, often using Extract, Transform, Load (ETL) processes, to pull this information into a central repository, such as a data lake or data warehouse. This process must ensure data quality, consistency, and the synchronization of timestamps across different systems.
3. Creation of a Unified Client Record ▴ The central repository must facilitate the creation of a single, unified view for each client. This involves linking a client’s static data (from the CRM) with their transactional data (from the OMS/EMS) and their associated costs (from fee engines). This unified record is the foundational element upon which all subsequent analysis is built.
4. Implementation of Monitoring and Alerting Logic ▴ The analytics engine sitting on top of the data repository must be programmed with rules that monitor for both poor execution quality and potential poor value outcomes. For example, alerts could be triggered for:
   • An execution whose slippage exceeds a defined threshold for that asset class.
   • A client paying a total level of fees that is an outlier compared to other clients with similar portfolios.
   • A high concentration of complaints related to a specific product.
   • A client with identified vulnerabilities being sold a product designated as complex.
5. Governance and Reporting Workflow ▴ The final operational step is to establish a clear workflow for how these alerts and monitoring outputs are handled. This includes defining escalation paths, assigning ownership for remedial actions, and creating comprehensive dashboards for review by product governance committees, best execution committees, and the board.

Quantitative Modeling and Data Analysis

The core of the evidence lies in the granular data points collected and the models used to analyze them. The following tables detail the specific data required to bridge the gap between MiFID II’s execution focus and the Consumer Duty’s value assessment.

Table 1 ▴ Core Data Points for Integrated Compliance

Predictive Scenario Analysis

Consider a scenario to illustrate the integrated data framework in action. An investment firm has a 72-year-old client, flagged in the CRM as ‘Potentially Vulnerable’ due to age and self-declared low investment experience. The firm’s adviser recommends a structured product linked to a basket of technology stocks, which has an annual management charge of 1.5% and an embedded derivative component.

A purely MiFID II-focused compliance check might pass without issue. The execution of the purchase order could be flawless, with minimal slippage against the arrival price and competitive explicit costs. The TCA report would look clean. The firm could demonstrate it had taken all sufficient steps to get the best execution result on that specific transaction.

However, an integrated Consumer Duty analysis, powered by the unified data framework, would raise several red flags. The system would cross-reference the client’s vulnerability flag and low experience level with the product’s own ‘high complexity’ rating from the Product Master File. It would trigger an alert based on a rule that questions the suitability of high-complexity products for clients with these characteristics. Furthermore, the analytics engine would calculate the product’s Total Cost of Ownership and compare it to simpler, lower-cost equity funds available on the firm’s platform.

This comparison would form a key part of the fair value assessment. The system would also scan the communications log to ensure the risks of the embedded derivative were explained in exceptionally clear terms, avoiding jargon. If the client later filed a complaint stating they did not understand the product’s downside risk, that unstructured data point would be linked to the client’s record, providing further evidence that a poor outcome had occurred. The governance dashboard would present this entire picture ▴ clean execution, but significant concerns around suitability, understanding, and fair value ▴ to the Product Governance Committee for immediate review and potential remedial action.

System Integration and Technological Architecture

The technological backbone for this compliance model requires a modern, layered data architecture. It is not a single piece of software but an ecosystem of integrated components.

1. Data Sources Layer ▴ This is the foundational layer comprising the operational systems of the firm (OMS, EMS, CRM, etc.). The critical requirement here is the ability to export data in a structured format (e.g. CSV, JSON) or via APIs, with high-fidelity, synchronized timestamps.
2. Ingestion & ETL Layer ▴ This layer consists of tools that extract data from the sources, transform it into a consistent format, and load it into the central repository. This is where data quality checks, normalization, and cleansing occur.
3. Central Data Repository (Data Lake/Warehouse) ▴ This is the heart of the system. A data lake is often preferred for its ability to store vast amounts of structured and unstructured data in its native format. This is where the unified client record is constructed and maintained.
4. Analytics & Modeling Engine ▴ This layer sits atop the repository and contains the business logic. It runs the TCA calculations, computes TCO, segments clients, and executes the monitoring rules that generate alerts. This may involve statistical programming languages like Python or R, or specialized vendor software.
5. Presentation Layer (Compliance Dashboard) ▴ This is the user interface for compliance officers, risk managers, and governance committees. It must provide intuitive visualizations, drill-down capabilities, case management tools for investigating alerts, and automated reporting features to evidence compliance to regulators.

This architecture ensures that data flows seamlessly from the point of client interaction and trade execution through to the highest levels of firm governance, creating an unbroken chain of evidence that addresses the distinct yet interconnected demands of both MiFID II and the Consumer Duty.

Reflection

Beyond Evidence a System of Outcomes

The assembly of the data points and architectural components detailed here provides the necessary toolkit for evidencing compliance. Yet, the ultimate objective transcends the mere avoidance of regulatory sanction. The true potential of an integrated data framework lies in its ability to create a positive feedback loop, transforming a firm’s understanding of its own operations and its relationship with its clients.

When execution quality metrics are no longer isolated statistics but are instead viewed as direct inputs into the client’s experience of value, the entire firm begins to operate differently. Trading desks become more attuned to the total cost impact of their decisions. Product designers gain a clearer, data-driven picture of how product features translate into real-world outcomes for specific client segments. Governance committees can move from reactive problem-solving to proactive optimization of the firm’s value proposition.

Does your current data infrastructure provide a single, coherent narrative of client value, or does it merely offer fragmented evidence of execution? The answer to that question will determine whether the significant investment required by these regulations becomes a burdensome cost center or the foundational blueprint for a more resilient, client-centric, and ultimately more profitable enterprise.