Data Quality Index

Effective banking supervision requires reliable reporting. Five years after the inception of the Single Supervisory Mechanism (SSM), a considerable number of banks do not yet provide the supervisors with data of sufficient quality. The European Central Bank’s (ECB’s) data quality framework aims to remedy this problem, through an increased interaction between the ECB and significant supervised banks at the highest level of consolidation, focusing on the following data quality dimensions: punctuality, accuracy, consistency, completeness, stability and plausibility.

These indicators are combined to produce the Data Quality Index (DQI), with values ranging from 1 (good) to 4 (very bad, missing).

The banks’ DQIs will be published in the information management system (IMAS) on a quarterly basis. Furthermore, these indicators and the derived DQI form the basis of individual ‘dashboards’, that summarise the DQI, assess and rate data quality performance, and provide an individual and peer-group comparison, which will be sent to joint supervisory team (JST) coordinators, also on a quarterly basis.

The information provided can be aggregated for all institutions or broken down by size, risk, country, income source or location of assets.

If a bank fails to deliver consistently suitable quality data in a timely manner, a five-step escalation process may be initiated. The first step involves an informal notification by the national competent authority (NCA) to provide or resubmit data. The next two steps involve letters signed by ECB managers. The last two steps entail enforcement measures or sanction proceedings. If the bank in question doesn’t fulfil its duty to submit, resubmit, correct or confirm information, the escalation process consequentially results in supervisory sanctions, e.g., an increase in regulatory capital requirements.

Punctuality refers to the time lag between the remittance date and the actual submission of the data from the NCA to the ECB. Metrics such as ‘average number of days of delay in receiving units of observation’ or ‘concentration of number of days of delay per level of aggregation’ will be used.

Accuracy concerns compliance with the EBA validation rules. It is quantified using the number of failing validation rules and completeness checks.

To check for consistency, logical relations between different subsets of data and their correspondence with the institution’s master data (internal consistency) and their correspondence with other published data (external consistency) will be evaluated.

Completeness is defined as the availability of the largest and most material subset of the required information. Metrics for completeness include the number of missing modules, the number of missing templates, and the number of missing data points.

The stability of data is analysed by investigating quarterly fluctuations in the reported data points and resulting values and in the number of countries that have been reported in geographical breakdowns in common reporting (COREP) and financial reporting (FINREP) and in the number of significant currencies reported as part of the additional monitoring metrics for liquidity reporting (AMM) and the net stable funding ratio (NSFR).

Plausibility refers to outliers and is measured through variance analyses over time and across peers. The ECB identifies values for which the NCAs must request explanations from the respective banks.

Data quality weaknesses in each of the ECB dimensions could be identified using suitable software. Punctuality could be enhanced through features for commenting and revising reported data. To ensure completeness, a documentation feature might be included that displays the progress of the successful reporting within the scope of the dashboard. Stability could be checked by an automated process that indicates irregular fluctuations with respect to previously reported values and plausibility through automated indication of outliers. Through enhanced (cross-)validation checks and appropriate interfaces for importing external data (or pre-existing reports), both accuracy and consistency could be improved.

Source:

https://www.bankingsupervision.europa.eu/press/conferences/sup_rep_conf/shared/pdf/2017/Data_quality_framework_tools_and_products.pdf

https://www.bankingsupervision.europa.eu/press/conferences/sup_rep_conf/shared/pdf/2017/Data_quality_developments.pdf

https://www.bankingsupervision.europa.eu/press/conferences/sup_rep_conf/shared/pdf/2017/Data_quality_escalation_process.pdf

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