Sampling Audit Evidence Using a Naive Bayes Classifier

arXiv:2403.14069v1 Announce Type: new
Abstract: Taiwan's auditors have suffered from processing excessive audit data, including drawing audit evidence. This study advances sampling techniques by integrating machine learning with sampling. This machine learning integration helps avoid sampling bias, keep randomness and variability, and target risker samples. We first classify data using a Naive Bayes classifier into some classes. Next, a user-based, item-based, or hybrid approach is employed to draw audit evidence. The representativeness index is the primary metric for measuring its …

abstract advances arxiv audit bayes bias classifier cs.lg data evidence integration machine machine learning processing randomness samples sampling study taiwan type