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Guide to internal models
Published: 14/06/2023
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Guide to internal models
Published: 04/03/2024
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Guide to internal models
Published: 01/10/2019
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Guide to internal models
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CRR ToolDelegated Regulation 2022/439
Article 73

Article 73 - Data quality

Article 73

Data quality

1.   When assessing the quality of internal, external or pooled data necessary to effectively support credit risk measurement and management process in accordance with Article 144(1)(d) and Article 176 of Regulation (EU) No 575/2013, competent authorities shall verify:

(a)

the completeness of values in the attributes that require them;

(b)

the accuracy of data ensuring that the data is substantively error-free;

(c)

the consistency of data ensuring that a given set of data can be matched across different data sources of the institution;

(d)

the timeliness of data values ensuring that the values are up-to-date;

(e)

the uniqueness of data ensuring that the aggregate data is free from any duplication given by filters or other transformations of source data;

(f)

the validity of data ensuring that the data is founded on an adequate system of classification, rigorous enough to compel acceptance;

(g)

the traceability of data ensuring that the history, processing and location of data under consideration can be easily traced.

2.   When assessing the data quality management process, competent authorities shall verify that:

(a)

all of the following are in place:

(i)

adequate data quality standards that set the objectives and the overall scope of the data quality management process;

(ii)

adequate policies, standards and procedures for data collection, storage, migration, actualisation and use;

(iii)

a practice of continuously updating and improving of the data quality management process;

(iv)

a set of criteria and procedures for determining conformity with the data quality standards, and in particular the general criteria and process of data reconciliation across and within systems including among accounting and internal ratings-based data;

(v)

adequate processes for internally assessing and constantly improving data quality, including the process of issuing internal recommendations to address problems in areas which need improvement and the process of implementing these recommendations with a priority based on their materiality and in particular the process for addressing material discrepancies arising during the data reconciliation process;

(b)

there is a sufficient degree of independence of the data collection process from the data quality management process, including a separation of the organizational structure and staff, where appropriate.