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

Recent studies have indicated that companies are increasingly experiencing Data Quality (DQ) related problems as more and more complex data are being collected. In order to address such problems, literature suggests the implementation of a Total Data Quality Management Program (TDQM) that should consist of the following phases: data quality definition, measurement, analysis and improvement. DQ is often defined as “fitness for use”. Although “fitness for use” captures the essence of quality, it is difficult to measure DQ using this broad definition. Thus, it has long been acknowledged that the quality of data is best described or analyzed via multiple attributes or dimensions. Yet, despite broad discussion in the DQ literature, there is no one definite set and exact definition of DQ dimensions because DQ is context dependent. Therefore, DQ dimensions should be identified and defined in relation to tasks to achieve a suitable level of DQ. This work identifies the important DQ dimensions for evaluating the quality of the data for credit risk assessment. It also explores the key DQ challenges and causes of DQ problems in financial institutions. Findings of a statistical analysis of an empirical study identify nine important DQ dimensions including accuracy and security for assessing the quality of the data in credit risk databases.