# How Do Regulators Assess the Adequacy of an Institution's Data Infrastructure for an Ima? ▴ Question

**Published:** 2025-08-05
**Categories:** Question

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## Concept

An inquiry into how regulators assess a financial institution’s [data infrastructure](https://prime.greeks.live/area/data-infrastructure/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) for an [Internal Model Approach](https://prime.greeks.live/area/internal-model-approach/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) (IMA) approval moves directly to the heart of the modern financial architecture. This process is a foundational examination of an institution’s capacity to generate trustworthy risk intelligence. The permission to use an internal model is predicated on an institution demonstrating that its data systems are not merely repositories of information, but are integrated, governed, and validated systems capable of producing capital calculations that command regulatory confidence. The assessment, therefore, is an architectural review of the highest order, designed to verify that the data flowing through the institution’s veins is pure, consistent, and fit for the critical purpose of ensuring systemic stability.

Regulators approach this task by deconstructing the institution’s data ecosystem into three primary pillars of inquiry. The first is Governance, which scrutinizes the human and organizational systems that oversee the data. This involves a deep look into the lines of responsibility, the independence of the [risk control unit](https://prime.greeks.live/area/risk-control-unit/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite), and the formal processes for model validation and management. The second pillar is [Data Quality](https://prime.greeks.live/area/data-quality/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite), a granular analysis of the data itself.

Here, regulators demand verifiable proof of data accuracy, completeness, timeliness, and, most importantly, its lineage ▴ the ability to trace a piece of data from its origin to its final use in a risk report. The final pillar is the Technological Infrastructure, an evaluation of the physical and logical systems that store, process, and secure the data. This pillar ensures the institution possesses the computational power, security protocols, and system robustness required to operate a complex [IMA](https://prime.greeks.live/area/ima/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) framework reliably.

> The regulatory assessment of an IMA data infrastructure is a comprehensive audit of an institution’s ability to produce reliable, verifiable, and timely risk data.

The Fundamental Review of the Trading Book ([FRTB](https://prime.greeks.live/area/frtb/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite)) has intensified this scrutiny, introducing stringent new standards that elevate the importance of a coherent data strategy. The framework’s requirements, such as the Profit and Loss (P&L) Attribution test and the criteria for assessing the modellability of risk factors, are functionally impossible to meet without a superior data infrastructure. An institution seeking IMA approval must therefore present its data architecture as a strategic asset, a purpose-built system designed for the production of high-fidelity risk information. The [regulatory assessment](https://prime.greeks.live/area/regulatory-assessment/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) is the final, exacting validation of that system’s design and operational integrity.

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## Strategy

Successfully navigating the regulatory assessment of an IMA data infrastructure requires a deliberate and forward-looking strategy that embeds the principles of integrity, control, and transparency deep within the institution’s operating model. An institution’s strategy must anticipate the precise lines of regulatory inquiry and build a framework that addresses them proactively. This involves creating a cohesive ecosystem where governance, data quality, and technology are not managed in silos, but are integrated components of a unified risk information architecture.

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## The Governance and Control Framework

A robust governance framework is the bedrock of a defensible IMA data infrastructure. Regulators must see a clear and unambiguous organizational structure with well-defined lines of responsibility for data ownership, model validation, and risk reporting. The strategy here is to establish and document a system of checks and balances that ensures the integrity of the risk calculation process from end to end.

- **Independent [Risk Control](https://prime.greeks.live/area/risk-control/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) Unit** The institution must demonstrate that its risk control unit is completely separate and independent from the trading business areas. This unit is responsible for the design, implementation, and performance of the [internal model](https://prime.greeks.live/area/internal-model/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite), ensuring that its calculations are objective and uninfluenced by business pressures. 

- **[Senior Management](https://prime.greeks.live/question/what-is-the-role-of-middle-management-in-sustaining-a-compliance-culture-set-by-senior-leaders/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) Oversight** The strategy must ensure and document the active involvement of senior management and the management body in the risk-control process. This includes approving all relevant policies, procedures, and methodologies related to the internal models and taking corrective action when weaknesses are identified. 

- **Comprehensive Documentation** An institution must maintain meticulous documentation for all aspects of its internal model. This includes policies, procedures, methodologies, and validation reports. The documentation must be of sufficient quality and detail to allow a qualified third party, such as a regulator, to understand and replicate the model’s processes. 

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## What Is the Strategy for Ensuring Data Quality?

Data quality is a non-negotiable prerequisite for IMA approval. The strategic objective is to create an environment where “golden sources” of data are established and maintained, eliminating duplicative and inconsistent datasets that create operational risk and undermine the credibility of risk calculations. This strategy focuses on ensuring that every critical data point is accurate, complete, and timely.

> A successful data quality strategy hinges on establishing verifiable data lineage, allowing every data point to be traced from its source to its use in risk reporting.

Regulators will rigorously test the institution’s data quality management. This includes verifying the processes for handling missing data, the criteria for using proxies, and the controls that prevent the use of erroneous or stale data. The P&L Attribution test under FRTB is a direct, high-stakes test of data consistency between the front-office trading systems and the risk management models. A strategic focus on data integrity is therefore essential for passing this test and securing IMA approval. 

### Strategic Pillars for IMA Data Infrastructure Readiness

| Pillar | Strategic Objective | Key Regulatory Focus Areas |
| --- | --- | --- |
| Governance | Establish clear ownership, oversight, and accountability for the entire risk data lifecycle. | Independence of the Risk Control Unit, Senior Management involvement, quality of internal documentation, and validation processes. |
| Data Quality | Ensure data is accurate, complete, timely, and has a verifiable lineage from source to report. | Data sourcing, proxy usage policies, data cleansing procedures, and reconciliation between risk and finance data. |
| Technology | Build a robust, scalable, and secure IT architecture capable of supporting complex risk calculations and data volumes. | System capacity for full revaluation, data storage solutions, disaster recovery plans, and IT security protocols. |

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## How Does Technology Architecture Support IMA Compliance?

The technology that underpins the data infrastructure must be fit for purpose. The strategy here involves investing in systems that can handle the immense data volumes and computational complexity demanded by FRTB’s IMA. This includes the capacity for full revaluation-based approaches and the storage of vast amounts of historical market data for back-testing and modellability assessments.

Regulators will assess the robustness of the IT systems, including their security, reliability, and the adequacy of disaster recovery and business continuity plans. The technology strategy must align with the broader goals of data integrity and control, ensuring that the systems architecture is a source of strength, not a point of failure, in the institution’s IMA framework.

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## Execution

The execution of a regulatory assessment is a meticulous and evidence-based process. An institution must be prepared to provide concrete proof of its data infrastructure’s adequacy through extensive documentation, system demonstrations, and detailed reports. The process moves beyond high-level strategy to the granular details of operational execution, where the theoretical soundness of the framework is tested against its practical implementation.

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## The Supervisory Assessment Process

Regulators follow a structured methodology to verify an institution’s compliance. This process is designed to be comprehensive and harmonized, ensuring a consistent level of scrutiny across institutions. It typically involves several distinct phases:

- **Documentation Review** The initial phase involves the submission of a vast array of documents. This includes internal policies, procedural manuals, validation reports, system architecture diagrams, and detailed inventories of risk factors, pricing models, and data sources. Regulators use this documentation to build a comprehensive understanding of the institution’s framework before any on-site inspection. 

- **On-Site Inspection and Interviews** Following the documentation review, supervisors will conduct on-site visits. This phase involves interviews with key personnel, including senior management, heads of the risk control unit, and IT staff. It also includes live demonstrations of the systems, where the institution must show the end-to-end flow of data, from capture to its use in the risk models. 

- **Targeted Testing and Evidence Gathering** During the inspection, regulators will perform their own tests. This can include requesting one-off calculations to assess the impact of a non-modelled risk factor, comparing the volatility generated by a proxy against observed market data, or recalculating P&L attribution metrics to verify the institution’s results. 

- **Findings and Remediation** The assessment concludes with a formal report of the regulator’s findings. If deficiencies are identified, the institution will be required to develop and execute a detailed remediation plan within a specified timeline. Approval for the IMA is contingent upon the successful resolution of all material findings. 

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## Key Data Quality Dimensions and Regulatory Metrics

Regulators demand objective evidence that the institution’s data meets the highest standards of quality. The following table outlines the key dimensions of this assessment and the types of metrics and evidence required.

### Data Quality Dimensions and Regulatory Metrics

| Data Quality Dimension | Regulatory Expectation | Example Metric or Test | Required Evidence |
| --- | --- | --- | --- |
| Completeness | Time series data used for risk calculations must be complete, with robust and documented procedures for handling missing data points. | Percentage of time series with missing data points before and after cleansing; number of consecutive days with no change. | Internal policies on data cleansing; logs of data filling activities; reports from periodic data quality checks. |
| Accuracy | Data used in the risk model must be accurate and reconciled with data from other sources, such as accounting systems. | Reconciliation break rates between risk and finance systems; frequency and magnitude of data correction events. | Reconciliation reports; documentation of error investigation and rectification processes. |
| Timeliness | Data must be captured and processed in a timely manner to ensure that risk calculations reflect the current state of the market and the institution’s portfolio. | Time-stamping of data from source to model input; analysis of delays in data feeds. | System logs; data flow diagrams with processing times; service level agreements (SLAs) with data vendors. |
| Lineage | The institution must be able to trace every data point from its origin to its final use, including all transformations and adjustments. | Full trace of a sample data point from a source system (e.g. trade capture) through all transformations to its appearance in a final risk report. | Data lineage documentation; inventories of data sources; documented transformation logic. |
| Proxy Soundness | The use of proxies for missing or insufficient data must be justified, conservative, and well-documented. | Correlation analysis between proxy data and actual data; volatility comparison of proxied vs. non-proxied time series. | Internal policy on proxy usage; documentation for each proxy approach; validation reports testing proxy conservatism. |

> The execution phase of the assessment requires institutions to provide verifiable evidence of compliance across all data quality dimensions.

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## IT Infrastructure Assessment Criteria

The IT systems supporting the IMA are a critical area of review. Regulators must be convinced that the technology is robust, secure, and capable of performing its functions with integrity. The assessment covers the entire IT ecosystem related to the market risk model.

- **System Robustness and Reliability** The competent authority will verify that the IT systems are robust enough to handle errors during execution and that appropriate remediation capabilities are in place in case of a system breakdown. The track record over the last 250 business days is a key area of analysis. 

- **Data Storage and Reconciliation** The assessment will verify that the institution can reconcile all internal model positions and instruments between the risk model and end-of-day value systems at least weekly. Any unreconciled items must be fully documented and monitored. 

- **Security and Access Control** The IT infrastructure must have strong security measures to protect the integrity and confidentiality of the data. This includes access controls, encryption, and monitoring for unauthorized activity. 

- **Outsourcing Arrangements** If any part of the IT solution or data processing is outsourced to a third-party vendor, regulators will scrutinize the arrangement to ensure it does not hinder their ability to assess compliance. The institution must demonstrate sufficient in-house knowledge and oversight of the outsourced functions. 

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## References

- European Banking Authority. “Final report on Draft regulatory technical standards on the assessment methodology under which competent authorities verify an institution’s compliance with the internal model approach.” EBA/RTS/2023/05, 21 November 2023.

- Treliant. “Reviewing The FRTB Data Requirements And How Firms Can Efficiently Develop A Data Strategy.” 29 June 2021.

- Basel Committee on Banking Supervision. “Minimum capital requirements for market risk.” January 2019.

- Basel Committee on Banking Supervision. “BCBS 239 – Principles for effective risk data aggregation and risk reporting.” January 2013.

- McNeil, Alexander J. Rüdiger Frey, and Paul Embrechts. Quantitative Risk Management Concepts, Techniques and Tools. Princeton University Press, 2015.

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## Reflection

The regulatory examination of an IMA data infrastructure compels an institution to look inward, to evaluate the very systems that produce its understanding of risk. Viewing this process solely as a compliance mandate is a strategic limitation. The true opportunity lies in using the regulatory framework as a blueprint for building a superior operational architecture. An infrastructure that satisfies the exacting standards of an external supervisor is, by its nature, an infrastructure capable of delivering a significant strategic edge.

It provides the foundation for more accurate risk measurement, more efficient capital allocation, and more confident decision-making. The ultimate goal is to construct a data ecosystem so robust and transparent that its outputs are trusted implicitly, not because they have passed an assessment, but because they are the product of a demonstrably superior system of intelligence.

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## Glossary

<a href="https://prime.greeks.live/area/internal-model-approach/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
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## Internal Model Approach

Meaning ▴ The Internal Model Approach (IMA) defines a sophisticated regulatory framework that permits financial institutions to calculate their capital requirements for various risk categories, such as market risk, credit risk, or operational risk, utilizing their own proprietary quantitative models and methodologies. </a><a href="https://prime.greeks.live/area/data-infrastructure/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
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## Data Infrastructure

Meaning ▴ Data Infrastructure refers to the comprehensive technological ecosystem designed for the systematic collection, robust processing, secure storage, and efficient distribution of market, operational, and reference data. </a><a href="https://prime.greeks.live/area/risk-control-unit/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
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## Risk Control Unit

Meaning ▴ A Risk Control Unit is a foundational architectural component within an electronic trading system, engineered to enforce pre-defined, real-time risk parameters and prevent the initiation or continuation of trading activity that violates established exposure thresholds. </a><a href="https://prime.greeks.live/area/data-quality/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
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## Data Quality

Meaning ▴ Data Quality represents the aggregate measure of information's fitness for consumption, encompassing its accuracy, completeness, consistency, timeliness, and validity. </a><a href="https://prime.greeks.live/area/ima/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
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## Ima

Meaning ▴ Intelligent Market Access, or IMA, designates a sophisticated, data-driven algorithmic framework engineered for the optimal routing and execution of institutional orders across fragmented digital asset markets. </a><a href="https://prime.greeks.live/area/regulatory-assessment/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
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## Regulatory Assessment

Meaning ▴ The Regulatory Assessment denotes the systematic process of evaluating a firm's adherence to established financial regulations, compliance frameworks, and operational standards within the institutional digital asset derivatives landscape. </a><a href="https://prime.greeks.live/area/frtb/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
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## Frtb

Meaning ▴ FRTB, or the Fundamental Review of the Trading Book, constitutes a comprehensive set of regulatory standards established by the Basel Committee on Banking Supervision (BCBS) to revise the capital requirements for market risk. </a><a href="https://prime.greeks.live/area/internal-model/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
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## Internal Model

Meaning ▴ An Internal Model is a proprietary computational construct within an institutional system designed to quantify specific market dynamics, risk exposures, or counterparty behaviors based on an organization's unique data, assumptions, and strategic objectives. </a><a href="https://prime.greeks.live/area/risk-control/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
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## Risk Control

Meaning ▴ Risk Control defines systematic policies, procedures, and technological mechanisms to identify, measure, monitor, and mitigate financial and operational exposures in institutional digital asset derivatives. </a><a href="https://prime.greeks.live/question/what-is-the-role-of-middle-management-in-sustaining-a-compliance-culture-set-by-senior-leaders/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
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## Senior Management

Middle management sustains compliance culture by translating senior leadership's strategic protocols into executable, team-specific operational code. </a>

**Tags:** [FRTB](https://options.greeks.live/area/frtb/), [Supervisory Review](https://options.greeks.live/area/supervisory-review/), [Data Governance](https://options.greeks.live/area/data-governance/), [Expected Shortfall](https://options.greeks.live/area/expected-shortfall/), [Risk Factor Modellability](https://options.greeks.live/area/risk-factor-modellability/), [Data Lineage](https://options.greeks.live/area/data-lineage/)	

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The P&L Attribution Test forces a systemic overhaul of a bank's infrastructure, mandating the unification of pricing and risk models.

- 
### [How Do Automated Data Validation Rules Enhance the Accuracy of Block Trade Regulatory Submissions?](https://options.greeks.live/question/how-do-automated-data-validation-rules-enhance-the-accuracy-of-block-trade-regulatory-submissions/)
![A precisely balanced transparent sphere, representing an atomic settlement or digital asset derivative, rests on a blue cross-structure symbolizing a robust RFQ protocol or execution management system. This setup is anchored to a textured, curved surface, depicting underlying market microstructure or institutional-grade infrastructure, enabling high-fidelity execution, optimized price discovery, and capital efficiency.](https://options.greeks.live/wp-content/uploads/2025/07/prime-os-engineered-rfq-block-trade-execution-for-digital-asset-derivatives.jpg)

Automated data validation rules meticulously enforce data integrity, ensuring precise block trade regulatory submissions and mitigating compliance risks.

- 
### [How Does a Security Master Reduce Operational Risk in Derivatives Trading?](https://options.greeks.live/question/how-does-a-security-master-reduce-operational-risk-in-derivatives-trading/)
![Visualizes the core mechanism of an institutional-grade RFQ protocol engine, highlighting its market microstructure precision. Metallic components suggest high-fidelity execution for digital asset derivatives, enabling private quotation and block trade processing. This system minimizes slippage and optimizes price discovery for Bitcoin options.](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-execution-engine-core-1.jpg)

A Security Master reduces operational risk by architecting a centralized, "golden source" of data that ensures firm-wide consistency for derivatives.

- 
### [What Are the Critical Data Governance Considerations for Institutional Block Trade Reporting?](https://options.greeks.live/question/what-are-the-critical-data-governance-considerations-for-institutional-block-trade-reporting/)
![Precision metallic pointers converge on a central blue mechanism. This symbolizes Market Microstructure of Institutional Grade Digital Asset Derivatives, depicting High-Fidelity Execution and Price Discovery via RFQ protocols, ensuring Capital Efficiency and Atomic Settlement for Multi-Leg Spreads.](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-protocols-for-precision-execution.jpg)

Robust data governance ensures the integrity and auditability of institutional block trade reporting, securing compliance and market trust.

- 
### [What Are the Practical Implications for Counterparty Data Management under the New DPE Framework?](https://options.greeks.live/question/what-are-the-practical-implications-for-counterparty-data-management-under-the-new-dpe-framework/)
![Abstract geometric forms, including overlapping planes and central spherical nodes, visually represent a sophisticated institutional digital asset derivatives trading ecosystem. It depicts complex multi-leg spread execution, dynamic RFQ protocol liquidity aggregation, and high-fidelity algorithmic trading within a Prime RFQ framework, ensuring optimal price discovery and capital efficiency.](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-protocol-liquidity-aggregation-market-microstructure-high-fidelity-execution-alpha-generation-prime-os-capital-efficiency.jpg)

The DPE Framework transforms counterparty data into a strategic asset through auditable provenance and intelligent enrichment.

- 
### [How Does a Unified Data Architecture Improve a Firm’s Ability to Respond to Future Regulatory Changes?](https://options.greeks.live/question/how-does-a-unified-data-architecture-improve-a-firms-ability-to-respond-to-future-regulatory-changes/)
![A sophisticated, symmetrical apparatus depicts an institutional-grade RFQ protocol hub for digital asset derivatives, where radiating panels symbolize liquidity aggregation across diverse market makers. Central beams illustrate real-time price discovery and high-fidelity execution of complex multi-leg spreads, ensuring atomic settlement within a Prime RFQ.](https://options.greeks.live/wp-content/uploads/2025/07/institutional-grade-rfq-protocol-facilitating-atomic-settlement-and-high-fidelity-execution-of-multi-leg-spreads.jpg)

A unified data architecture provides a single, coherent data framework, enabling rapid, accurate, and auditable responses to regulatory changes.

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---

**Original URL:** https://options.greeks.live/question/how-do-regulators-assess-the-adequacy-of-an-institutions-data-infrastructure-for-an-ima/
