An Autonomous Control Framework for Data Quality Governance in Trading and Market Risk Monitoring

Author

Anubhav Birendra Jha

Abstract

 Financial institutions increasingly depend on large, fast-moving volumes of reference, pricing, transaction, and valuation data to calculate market risk exposures, including Value-at-Risk (VaR), sensitivities, stress measures, and regulatory capital. Conventional data quality practice is often organized around reactive detection: controls identify exceptions after data have entered a processing chain, while remediation depends on manual investigation. This paper develops the Autonomous Control Framework (ACF), a conceptual maturity model describing progression from manual detection to rule-based monitoring, automated alerting, predictive anomaly detection, and bounded autonomous remediation at the level of Critical Data Elements (CDEs). The framework is grounded in BCBS 239 risk data aggregation principles, data quality and governance literature, and model risk management considerations. A key distinction is made between autonomous control execution and autonomous judgment: Stage 5 permits only pre-authorized, reversible, auditable actions such as quarantine or use of a documented fallback source, and does not delegate discretionary risk judgment to a machine. The paper is conceptual and illustrative rather than empirically validated. Its principal contribution is a structured bridge between CDE governance, detection latency, market-risk data propagation, and bounded automation, supported by explicit maturity dimensions, an autonomy-eligibility matrix, and testable research propositions. Future research should validate the framework using institutional data, controlled experiments, and expert-based assessment of maturity and autonomy eligibility.

Keywords

Data Quality Management, Data Governance, Critical Data Elements, Market Risk, BCBS 239, Model Risk; Anomaly Detection, Autonomous Controls, Risk Data Aggregation, Data Lineage.

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References

[1] Basel Committee on Banking Supervision, "Implementation of the Principles for effective risk data aggregation and risk reporting BCBS 239 Principles," Newsletter No. 36, Bank for Int. Settlements, Basel, Switzerland, Jan. 6, 2026

 [2] Board of Governors of the Federal Reserve System, Office of the Comptroller of the Currency, and Federal Deposit Insurance Corporation, "Revised guidance on model risk management," SR 26-2, Washington, DC, USA, Apr. 17, 2026.

[3] European Central Bank, "Guide on effective risk data aggregation and risk reporting," ECB Banking Supervision, Frankfurt am Main, Germany, May 2024.

[4] European Parliament and Council of the European Union, "Regulation (EU) 2024/1689 laying down harmonised rules on artificial intelligence (Artificial Intelligence Act)," Official J. Eur. Union, vol. L, Jul. 12, 2024.

[5] Basel Committee on Banking Supervision, "Progress in adopting the Principles for effective risk data aggregation and risk reporting," Bank for Int. Settlements, Basel, Switzerland, Nov. 2023.

[6] S. E. Whang, Y. Roh, H. Song, and J.-G. Lee, "Data collection and quality challenges in deep learning: A data-centric AI perspective," VLDB J., vol. 32, no. 4, pp. 791–813, Jul. 2023.

[7] National Institute of Standards and Technology, "Artificial Intelligence Risk Management Framework (AI RMF 1.0)," NIST AI 100-1, Gaithersburg, MD, USA, Jan. 2023.

[8] J. Martins, H. S. Mamede, and J. Correia, "Risk compliance and master data management in banking – A novel BCBS 239 compliance action-plan proposal," Heliyon, vol. 8, no. 6, Art. no. e09627, Jun. 2022.

[9] L. Ehrlinger and W. WoB, "A survey of data quality measurement and monitoring tools," Front. Big Data, vol. 5, Art. no. 850611, Mar. 2022.

[10] S. O. Pinto and V. A. Sobreiro, "Literature review: Anomaly detection approaches on digital business financial systems," Digit. Bus., vol. 2, no. 2, Art. no. 100038, 2022.

[11] G. Pang, C. Shen, L. Cao, and A. van den Hengel, "Deep learning for anomaly detection: A review," ACM Comput. Surv., vol. 54, no. 2, pp. 1–38, Mar. 2021.

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