A multidimensional analysis of data quality for credit risk management: New insights and challenges

  • Authors:

  • Helen-Tadesse Moges;Karel Dejaeger;Wilfried Lemahieu;Bart Baesens

  • Affiliations:

  • Department of Decision Sciences and Information Management, Katholieke Universiteit Leuven, Naamsestraat 69, B-3000 Leuven, Belgium;Department of Decision Sciences and Information Management, Katholieke Universiteit Leuven, Naamsestraat 69, B-3000 Leuven, Belgium;Department of Decision Sciences and Information Management, Katholieke Universiteit Leuven, Naamsestraat 69, B-3000 Leuven, Belgium;Department of Decision Sciences and Information Management, Katholieke Universiteit Leuven, Naamsestraat 69, B-3000 Leuven, Belgium and School of Management, University of Southampton, Southampton ...

  • Venue:
  • Information and Management
  • Year:
  • 2013

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Abstract

Recent studies have indicated that companies are increasingly experiencing Data Quality (DQ) related problems as more complex data are being collected. To address such problems, the literature suggests the implementation of a Total Data Quality Management Program (TDQM) that should consist of the following phases: DQ definition, measurement, analysis and improvement. As such, this paper performs an empirical study using a questionnaire that was distributed to financial institutions worldwide to identify the most important DQ dimensions, to assess the DQ level of credit risk databases using the identified DQ dimensions, to analyze DQ issues and to suggest improvement actions in a credit risk assessment context. This questionnaire is structured according to the framework of Wang and Strong and incorporates three additional DQ dimensions that were found to be important to the current context (i.e., actionable, alignment and traceable). Additionally, this paper contributes to the literature by developing a scorecard index to assess the DQ level of credit risk databases using the DQ dimensions that were identified as most important. Finally, this study explores the key DQ challenges and causes of DQ problems and suggests improvement actions. The findings from the statistical analysis of the empirical study delineate the nine most important DQ dimensions, which include accuracy and security for assessing the DQ level.