Control of the Business Intelligence area in credit institutions

The MNB has issued a Recommendation to help credit institutions to comply more effectively with these reporting obligations, detailing the MNB's expectations in the area of data asset management. As the audit of the year 2024 should already address the Recommendation, credit institutions will need to adapt quickly to these mandatory requirements.

What is the purpose of the recommendation?

To define expectations and good practices to be followed in order to improve the quality of data provision, to shorten the time needed for the data provision process and to provide guidance for the design and operation of an efficient data provision process.

A prerequisite for the quality of data provision is a conscious and well-organised management of the data assets of the institution as a whole.

Main topics of the Recommendation

The regulation deals in detail with the areas that provide data and the areas that serve them, in several aspects:

• organisation,

• regulation registry

• process

• responsibilities

• skills & competencies

• control & monitoring

• developments

Specific expectations of the MNB with examples:

• Organisation: the general expectation for the institution's overall data reporting process is to ensure that human resources are allocated in a way that is appropriate to the size of the institution and the complexity of the activity and data reporting process.

• Data dictionary, metadata repository: In order to achieve data transparency, efforts should be made to use and maintain a repository with uniform data definitions.

• Data errors, data corrections and data modifications: Designated persons responsible should assist the area providing the data.

• Reporting Inventory: A comprehensive, up-to-date inventory should be maintained, which should include, in a retrievable manner, the data reporting obligations to be fulfilled to the MNB.

• As a complement to the reporting inventory, it is recommended that detailed documentation on the data reporting, e.g. data sets used, data life cycle, data definitions, data reporting steps, etc., is provided.

• Derived data, indicators: It is good practice for the institution to ensure the accuracy of the basic data and the appropriateness of the calculation through manual or automatic verification steps.

• Incident register: a record of data quality problems and incidents reported by the MNB and arising from self-revisions. This should also record late data submissions, revisions, MNB comments.

• Data provision automation plan: an action plan should be developed to enable new or modified data provision. The action plan should be in line with the institution's IT strategy and be feasible.

3-pillar verification of compliance with the Recommendation:

• Self-assessment: a self-assessment checklist is included in the annexes to the Recommendation, together with instructions on how to complete it. The self-assessment must be carried out annually and submitted to the MNB by 31 December.

• Internal control: The internal control of the institution is carried out by the internal audit of the institution to verify the fulfilment of the expectations set out in the Recommendation and the compliance with the legislation and the adaptation of other standards.

• Auditor: The application of the Recommendation should be included in the annual special audit report.

Stratis Ltd. has extensive experience in designing and implementing enterprise-wide data asset management solutions, be it a BI strategy, an operating model, a transformation roadmap or their implementation.

Here are some examples of the specific tasks we can support our clients in complying with MNB Recommendation 19/2022:

• AS-IS survey:

  • Assessment of the client's baseline along the following aspects: governance, processes, organization, capabilities, architecture, technology

  • Data asset survey, documentation

  • DM Maturity Survey: based on international standards and complemented by our own experience, we present the level of data maturity of the company and possible improvement paths.

  • Digital transformation portfolio: gathering existing initiatives, identifying further opportunities ("use cases").

• DM strategy, vision, vision development: how the data asset management area can effectively support the achievement of the company's strategic objectives in the long term.

  • Identifying scenarios

  • Detailed development of scenarios selected by the client

  • DM operating model

• GAP analysis: examining the differences between the current state and the vision state, looking for root causes, identifying opportunities for improvement and optimisation.

• Transformation roadmap: Identification of the steps that can realistically be taken to achieve the DM vision states. Preparation of high-level resource and cost estimates.

• Supporting systems: design, selection, implementation support and definition of processes for systems supporting data asset management:

  • Metadata Repository

  • Data dictionary

  • Business Glossary

  • Glossary of Business Glossary

  • Glossary of KPIs and indicators, etc.

• Master data management concept creation, implementation support.

The related processes for our clients are based on international trends, industry standards and best practices, following the DAMA Framework guidelines for data asset management. Our BI developments and operating models are "tailor-made" to meet the client's needs, focusing on company-specific challenges, always based on individual assessments and analyses.

Our references include several members of the financial sector, credit institutions and insurance companies, so in addition to our professional knowledge, we can also provide our current and prospective clients with substantial experience in the challenges of data asset management.

Recommendation No. 19/2022 (XII.1.) of the National Bank of Hungary on the design, operation, framework and control functions of the compilation process of credit institutions' data reporting and related data asset management tasks. The Recommendation is addressed to credit institutions under the Hpt. and the EEA branch.