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.

In the previous post, we examined the value propositions that Appzen's AI brings to auditing expense reports. In this post, we analyze what insights we can extract from Appzen when it comes more broadly to applying AI to the external financial audit. The following gives a refresher on how the Appzen AI audit works: Based on this we look at a number of factors that exist in this process to develop Standardized process: The expense report process that has been fairly standardized for over a decade: employees submit a digitized report of what they spent, expense codes, commentary and all the supporting documentation (e.g. This is similar to how factories needed an assembly line before they could be automated. Standardized capture and presentation of audit evidence: I think this is a key piece: the actual audit evidence (i.e.