Finmatek Ltd

Requirement to Notify the Competent Authority

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As per Article 9(1)(a) of the ITS, the entity responsible for reporting should promptly (as soon as it becomes aware of them) notify its competent authority and, if different, also the competent authority of the reporting counterparty of any of the following instances:

  1. any misreporting caused by flaws in the reporting systems that would affect a significant number of reports,
  2. any reporting obstacle preventing the report submitting entity from sending reports to a Trade Repository within the deadline set out in the Article 9 of EMIR,
  3. any significant issue resulting in reporting errors that would not cause rejection by a trade repository in accordance with the RTS on data quality.

The notification should indicate at least the basic information on and identification of the notification, ERR and RSE(s), the scope of the affected reports, the type of the errors or omissions, the reasons for the errors or omissions, steps taken or planned to resolve the issue, the date of the occurrence, and the timeline for resolution of the issue and data submission or correction. The entity responsible for reporting should provide the notification in a common template published on ESMA website.

Calculating significant number of reports

Significant number of reports should be assessed separately for each of the following categories:

  • Category 1 – reports with action types ‘New’, ‘Modify’, ‘Correct’, ‘Terminate’, ‘Error’, ‘Revive’, ‘Position component’
  • Category 2 – reports with action type ‘Valuation’
  • Category 3 – reports with action type ‘Margin update

If the number of reports affected by the reporting issue is significant in at least one of the categories, the competent authorities should be notified of the reporting issue.

Number of reports affected by misreporting is significant if it exceeds the following threshold:

NumOfAffReports / AverageMonthNum > Y% and NumOfAffReports > X

i.e. NumOfAffReports >= Threshold = max {X; Y% of AverageMonthNum},

where X and Y are calibration constants, and AverageMonthNum is the average monthly number of submissions calculated on the day of assessment as

 

(NumOfReportsMonth-12 + NumOfReportsMonth-11 + … + NumOfReportsMonth-2 + NumOfReportsMonth-1) / 12 = NumOfReportsLast12Months / 12 using the actual numbers of reports submitted during the last 12 months.

Below are the buckets and thresholds provided by ESMA

 

Average monthly number of submissions (AverageMonthNum)

 

0<=A<100,000

100,000<=A<1 000 000

1 000 000>=A

X

100

20,000

1,500,000

Y%

20%

15%

10%

 

Data quality indicators

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Data quality indicators (DQIs) are essential metrics that enable organizations to assess and enhance the quality of the data reported.  By systematically monitoring the 19 Data Quality indicators published by ESMA, reporting entities can identify areas for improvement and ensure that their data is reliable.

DQIs ask six fundamental questions:

  • Accuracy: Does the information reported reflect the data accurately?
  • Completeness: Have all necessary data points been reported?
  • Consistency: Is there uniformity across all datasets?
  • Timeliness: Has the data been submitted within the relevant deadlines?
  • Validity: Is the data reported in the proper format?
  • Uniqueness: Has any duplicate data been reported?

By regularly addressing these questions, reporting entities can significantly enhance the quality of their reported data. This proactive approach not only improves data integrity but also helps prevent larger issues such as misbookings, incorrect reporting logic, inaccurate static data, incorrect pricing of transactions.

The table below provides an overview on the 19 data quality indicators and their methodology that are regularly calculated and monitored by ESMA. These indicators are calculated for each jurisdiction and distributed among the NCAs.

 

The need for post reporting monitoring

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EMIR REFIT signifies a major revision of EMIR regulation, bringing forth new reporting obligations designed to improve data quality. As a result, it is now more crucial than ever for reporting entities to closely monitor the accuracy of their reported data and conduct end-of-day reconciliations between their trading systems and the Trade Repository as poor data quality may lead to operational inefficiencies, potential regulatory penalties, and loss of Counterparty trust.

It is crucial therefore that Counterparties monitor the pairing and matching status of their reports. Continuous checks will ensure that data reported is complete, accurate, and consistent, while early detection of issues prevents larger problems (misbookings, incorrect reporting logic, incorrect static data, incorrect pricing of transactions etc.)

Furthermore, it is vital that Counterparties perform end of day reconciliations between their systems and the Trade repository. Comparing data reported ensures alignment with the firm’s systems, static data, counterparties and identifies any discrepancies reported. In addition, the reconciliation also serves as an audit trail for Compliance with the competent authorities.

In summary, reporting parties are required to have robust arrangements in place that include:

  • Testing Reporting Processes: Firms must regularly test their reporting processes to identify potential errors.
  • Regular Reconciliation: Firms should reconcile their trading records against submitted transaction reports to verify accuracy and completeness.
  • Monitoring of Pairing & Matching Status: Firms should establish processes to promptly identify and rectify errors in transaction reports which is essential for maintaining regulatory compliance.

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