Summary of Sampling Audit Evidence Using a Naive Bayes Classifier, by Guang-yih Sheu and Nai-ru Liu
Sampling Audit Evidence Using a Naive Bayes Classifier
by Guang-Yih Sheu, Nai-Ru Liu
First submitted to arxiv on: 21 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel approach to auditing by integrating machine learning with traditional sampling techniques. It aims to improve the representativeness of audit evidence while avoiding sampling bias. The authors use Naive Bayes classifier to classify data into classes, followed by user-based, item-based, or hybrid approaches to draw audit evidence. The key metric used to measure representativeness is the representativeness index. Three experiments demonstrate the benefits of machine learning integration in handling complex patterns, correlations, and unstructured data, as well as improving efficiency in sampling big data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Auditors are overwhelmed with too much data to process! This study helps by combining machine learning with traditional auditing methods. It uses special algorithms to sort data into categories, then picks the best samples from each group. The goal is to get a fair and balanced view of what’s going on, while avoiding mistakes. The results show that this new approach can handle big datasets and make things more efficient. |
Keywords
* Artificial intelligence * Machine learning * Naive bayes
