.png){kind=icon}
返回
Developing a decision support system to detect material weaknesses in internal control
.png){kind=icon}
PRE
AI
DOI：10.1016/j.dss.2021.113631.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Wells Fargo employees set up 3.5 million fraudulent accounts over several years. Wells Fargo ended up paying $4.5 billion in fines. The Wells Fargo scandal highlights the importance of management actions to prevent misstatements and potential frauds on a timely basis. This study utilized the design science research paradigm to develop a predictive framework (IT artifact) in order to stratify firms into multiple risk groups for disclosing material weakness(es) in internal control (MWIC). The proposed methodology employed a hybrid heuristic optimization-based machine learning methodology. Synthetic minority over-sampling technique (SMOTE) was utilized to handle the learning problem with the imbalanced data. The best performing model was the proposed hybrid Genetic Algorithms (GA) and Support Vector Machines (SVM). The proposed methodology was internally validated via k-fold cross validation, and then externally validated using several separate datasets. The (GA) selected variables were ranked from the most important to the least through Information fusion (IF) sensitivity. A web-based decision support system was built to predict the firm-specific MWIC risk category. The web-based tool can be used to create an early warning system for predicting MWIC(s).
Keyword:
Business analytics
Data mining
Decision support systems
Material weakness in internal control
期刊
IF:
6.8
论文数:
3.8K
被引数:
1.5W
机构
引用论文
A Bayesian Belief Network-based probabilistic mechanism to determine patient no-show risk categories
PREAI
分享收藏
分享收藏Hybrid genetic algorithms and support vector machines for bankruptcy prediction基于混合遗传算法和支持向量机的破产预测
PREAI
分享收藏Arsenic removal from aqueous solutions by adsorption using novel MIL-53(Fe) as a highly efficient adsorbent使用新型MIL-53(Fe) 作为高效吸附剂通过吸附从水溶液中去除砷.png)
RSC Advances
RSC AdvancesIF0
PREAI
分享收藏
查看更多内容