Open AccessA Fast Audit Doubt Finding Model
Author(s) -
Qing Wan,
Weibo Li,
Hairong Wang,
Hua Yan,
Rui Xiang
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1865/4/042065
Subject(s) - outlier , local outlier factor , anomaly detection , cluster analysis , computer science , pruning , data mining , audit , centroid , algorithm , artificial intelligence , accounting , biology , agronomy , business
In order to improve the audit efficiency and accuracy under the condition of big data, an audit doubt discovery model (CLOWF) based on an adaptive clustering outlier detection algorithm is proposed. The model first uses the slope ratio method to obtain the K value of the K-means++ algorithm, thereby effectively ensuring the clustering effect; At the same time, for the problem of slow pruning speed, a parallel calculation method for the data radius and centroid of each cluster is proposed and designed; Combined with the weighted local outlier factor detection algorithm to calculate the outlier factor, the data greater than the epsilon threshold of outlier is proposed as an indicator to determine the audit doubt. Through auditing application cases, comparing the local outlier factor detection algorithm and the cluster-based outlier detection algorithm, the results show that the execution time of the CLOWF model is the shortest and the accuracy rate is 97.3%.