Sampling Audit Evidence Using a Naive Bayes Classifier
Guang-Yih Sheu Nai-Ru Liu
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 representativeness. The user-based approach samples data
symmetric around the median of a class as audit evidence. It may be equivalent
to a combination of monetary and variable samplings. The item-based approach
represents asymmetric sampling based on posterior probabilities for obtaining
risky samples as audit evidence. It may be identical to a combination of
non-statistical and monetary samplings. Auditors can hybridize those user-based
and item-based approaches to balance representativeness and riskiness in
selecting audit evidence. Three experiments show that sampling using machine
learning integration has the benefits of drawing unbiased samples, handling
complex patterns, correlations, and unstructured data, and improving efficiency
in sampling big data. However, the limitations are the classification accuracy
output by machine learning algorithms and the range of prior probabilities.