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).

富国银行的员工在几年内设立了 350 万个欺诈账户。富国银行最终支付了 45 亿美元的罚款。富国银行丑闻凸显了管理层采取措施及时防止误报和潜在欺诈的重要性。本研究利用设计科学研究范式开发了一个预测框架（IT 工件），以便将公司分层为多个风险组，以披露内部控制 (MWIC) 中的重大缺陷。所提出的方法采用基于混合启发式优化的机器学习方法。合成少数过采样技术（SMOTE）被用来处理不平衡数据的学习问题。表现最好的模型是提议的混合遗传算法( GA ) 和支持向量机( SVM )。所提出的方法通过 k 折交叉验证进行内部验证，然后使用几个单独的数据集进行外部验证。(GA) 选择的变量通过信息融合( IF ) 敏感性从最重要到最不重要。建立了一个基于网络的决策支持系统来预测公司特定的 MWIC 风险类别。基于 Web 的工具可用于创建用于预测 MWIC 的预警系统。