Assessment of data quality in accounting data with association rules

Business rules are an effective way to control data quality. Business experts can directly enter the rules into appropriate software without error prone communication with programmers. However, not all business situations and possible data quality problems can be considered in advance. In situations where business rules have not been defined yet, patterns of data handling may arise in practice. We employ data mining to accounting transactions in order to discover such patterns. The discovered patterns are represented in form of association rules. Then, deviations from discovered patterns can be marked as potential data quality violations that need to be examined by humans. Data quality breaches can be expensive but manual examination of many transactions is also expensive. Therefore, the goal is to find a balance between marking too many and too few transactions as being potentially erroneous. We apply appropriate procedures to evaluate the classification accuracy of developed association rules and support the decision on the number of deviations to be manually examined based on economic principles.