TY - JOUR
T1 - Developing a decision support system to detect material weaknesses in internal control
AU - Nasir, Murtaza
AU - Simsek, Serhat
AU - Cornelsen, Erin
AU - Ragothaman, Srinivasan
AU - Dag, Ali
N1 - Publisher Copyright:
© 2021
PY - 2021/12
Y1 - 2021/12
N2 - 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).
AB - 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).
KW - Business analytics
KW - Data mining
KW - Decision support systems
KW - Material weakness in internal control
UR - http://www.scopus.com/inward/record.url?scp=85109040647&partnerID=8YFLogxK
U2 - 10.1016/j.dss.2021.113631
DO - 10.1016/j.dss.2021.113631
M3 - Article
AN - SCOPUS:85109040647
SN - 0167-9236
VL - 151
JO - Decision Support Systems
JF - Decision Support Systems
M1 - 113631
ER -