Opportunities and challenges of data management for the finance function within the banking sector in Luxembourg
Schoonbroodt, Estelle
Promotor(s) : Sougné, Danielle
Date of defense : 18-Jun-2019/20-Jun-2019 • Permalink : http://hdl.handle.net/2268.2/6478
Details
Title : | Opportunities and challenges of data management for the finance function within the banking sector in Luxembourg |
Translated title : | [fr] Opportunités et challenges de la gestion des données pour la fonction finance dans le secteur bancaire à Luxembourg |
Author : | Schoonbroodt, Estelle |
Date of defense : | 18-Jun-2019/20-Jun-2019 |
Advisor(s) : | Sougné, Danielle |
Committee's member(s) : | Esch, Louis
Hambuckers, Julien Wilhem, jean-Charles |
Language : | English |
Number of pages : | 146 |
Keywords : | [en] Data governance [en] Data quality [en] Data management [en] banking sector [en] Bank [en] Reporting [en] BCBS239 [en] Luxembourg |
Discipline(s) : | Business & economic sciences > Finance |
Funders : | KPMG Luxembourg |
Institution(s) : | Université de Liège, Liège, Belgique |
Degree: | Master en sciences de gestion, à finalité spécialisée en Banking and Asset Management |
Faculty: | Master thesis of the HEC-Ecole de gestion de l'Université de Liège |
Abstract
[en] Data governance is a promising approach for banks to improve data quality management and to comply with and meet regulatory requirements. In this rapidly evolving era, data became a critical asset which provide the holder with considerable power. Banks need effective data management to respond to the highly competitive environment, and must extract value from the data observations. In addition, although the regulatory context has been strengthened since the crisis, the authorities still require more granular information to be reported in order to enhance transparency.
This study was conducted under the lead of the advisory service of KPMG Luxembourg and the research focused on the Luxembourg banking sector; the purpose was to assess the surveyed banks’ data management strategy by identifying the main barriers. The data governance model is an opportunity to structure a bank with policies and standards, and to define roles and responsibilities which will definitely impact positively the data quality and analytics.
The results demonstrate a poor data-centric strategy which results in a lack of trust in the data. The banks interviewed did not have a structured approach to a governance model. The maturity of the subject was very low, whereas in this environment banks must have a much finer approach and seek more efficiency. The survey was prepared and carried under the watchful eye of the finance department, which is a data consumer. It does, however, appear that they are not committed to taking the lead on the subject. Banks are currently in a position to meet regulatory demands, but they are not proactively positioning themselves to capture opportunities.
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