2018年-ECB欧洲央行_AnaCredit_Validation_Checks_–_Selected_validation_checks_performed_in_AnaCredit_datasets_–_Version_13_38页_3mb

AnaCredit Validation Checks Summary

1. Introduction

This document provides detailed information on the main set of validation checks used to ensure the quality of AnaCredit data. It supplements the AnaCredit Reporting Manual and explains how data reported to AnaCredit must comply with the AnaCredit data model and the AnaCredit Regulation. The validation checks are divided into three main categories: Referential integrity, Completeness, and Consistency. These checks are not legally binding but are essential to ensure data completeness and consistency.

2. Main Categories of Validation Checks

2.1 Referential Integrity Checks

These checks ensure that the data reported to AnaCredit conforms to the AnaCredit data model and that all records associated with a single credit element exist across the relevant datasets. They are triggered based on the dataset being examined and may depend on specific conditions.

• Purpose: Ensure the existence of necessary records across different datasets.
• Execution: Triggered sequentially, not simultaneously, depending on the dataset.
• Key Points:
  • Data from resident foreign branches may be reported to two NCBs, but the overall data is subject to the same validation checks.
  • If not all templates are available, only the received ones are validated.

2.2 Completeness Checks

These checks verify that all expected attributes for an element are reported. They are divided into two subsets:

2.2.1 Completeness - Counterparty Reference Dataset

• Focus: Completeness of counterparty reference data attributes.
• Key Points:
  • Certain attributes may not be required if the counterparty does not meet the conditions specified.
  • LEI and national identifier are always checked for completeness.
  • Some checks were removed in version 1.3 according to AnaCredit Q&As.

2.2.2 Completeness - Credit Relevant Datasets

• Focus: Completeness of attributes in datasets other than the counterparty reference dataset.
• Key Points:
  • These checks consider scenarios from Annex II of the AnaCredit Regulation.
  • Derogations from NCBs are taken into account, and some checks were removed in version 1.3.

2.3 Consistency Checks

These checks ensure that the values of data attributes are consistent with each other, based on their relational interconnectedness. They verify that the reported data aligns with the conceptual logic of the AnaCredit data model.

• Purpose: Ensure internal consistency of the data.
• Key Points:
  • Certain concepts may not apply to specific attributes, such as the settlement date, which is only relevant if funds have been disbursed.
  • "Non-applicable" values are considered valid and are part of the validation process.
  • Some checks were removed in version 1.3 due to inconsistencies or redundancy.

3. Key Changes in Document Versions

4. Core Validation Checks

4.1 Referential Integrity

4.2 Completeness

• Removed Checks: CC0160, CC0170, CC0180 from Table A and B; CC0100, CC0110 from Table C and D.
• Added Checks: CD0060 (based on AnaCredit Q&As).
• Removed Checks: CT0350 (as it forms the primary key).

4.3 Consistency

• Removed Checks: CN0380, CN0842, CN0913, CN0540, CN0550, CN0829, CN0070, CN0090, CN0100, CN0120, CN0130, CN0320, CN0802, CN0808, CN0832, CN0833, CN0875, CN0914.
• Added Checks: CN0852, CN0865, CN0875, CN0876, CN0901, CN0913, CN0914.
• Adjusted Checks: CN0592, CN0650, CN0801, CN0802, CN0805, CN0806, CN0807, CN0809, CN0812, CN0813, CN0827.

5. General Validation Rules

• Syntax: Uses logical operators such as IF, THEN, WHERE, and expressions like [Dataset Attribute] = 'Value', [Dataset Attribute] <> 'Value', [Dataset Attribute] IN 'Value', and [Dataset Attribute] NOT IN 'Value'.
• Conditional Checks: Some checks are conditional, based on specific attributes or roles.
• Union Logic: Used to indicate that a record may exist in one of two datasets.
• Derogations: NCBs may apply derogations, which may exempt certain attributes from validation.
• Non-Applicable Values: Considered valid and relevant for consistency checks.

6. Conclusion

The AnaCredit Validation Checks are essential for ensuring data quality, completeness, and consistency. They are structured around the AnaCredit data model and the AnaCredit Regulation, and are designed to support accurate and reliable reporting. The checks are continuously refined to address inconsistencies, redundancies, and new requirements.