Enhancing data cleaning process on accounting data for fraud detection

Abstract

Data cleaning is a crucial step in fraud detection as it involves identifying and correcting any inaccuracies or inconsistencies in the data. This can help to ensure that the data being used for fraud detection is reliable and accurate, which in turn can improve the effectiveness of fraud detection algorithms. Due to the overwhelming amount of data, data cleaning specific for fraud detection is a very important activity for the auditor to find the appropriate information. Therefore, a new accounting data cleaning for fraud detection is needed. In this paper, an enhancement of the process of fraud detection by accounting auditors through the implementation of accounting data cleaning technique is proposed. The proposed technique was embedded in a prototype system called accounting data cleaning for fraud detection (ADCFD). Through experiment, the performance of the proposed technique through ADCF is compared with those obtained from the IDEA system, using the same dataset. The results show that the proposed enhanced technique through ADCFD system performed better than the IDEA system.