TY - GEN
T1 - Machine Learning Detection for Financial Statement Fraud
AU - Hwang, Ting Kai
AU - Chen, Wei Chun
AU - Chiang, Wan Chi
AU - Li, Yung Ming
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - This study intends to develop a methodology of fraudulent transaction detection model. The algorithm of XGBoost integrating the techniques of SMOTE sampling method and Bayesian Hyperparameter Optimization, is proposed to separate fraud transactions from non-fraud transactions. The experimental results based on the public data set of financial statement fraud from Kaggle website show the proposed model is better than the commonly used binary-classification methods, such as Logistic Regression, SVM, KNN, Random Forest, XGBoost without Hyperparameter Tuning and Multilayer Perceptron. The method of establishing fraud detection models assists people who lack the machine learning domain expertise for the modeling and tuning parameter techniques. It can help to detect abnormal transactions as early as possible and carry out risk management for banking industry.
AB - This study intends to develop a methodology of fraudulent transaction detection model. The algorithm of XGBoost integrating the techniques of SMOTE sampling method and Bayesian Hyperparameter Optimization, is proposed to separate fraud transactions from non-fraud transactions. The experimental results based on the public data set of financial statement fraud from Kaggle website show the proposed model is better than the commonly used binary-classification methods, such as Logistic Regression, SVM, KNN, Random Forest, XGBoost without Hyperparameter Tuning and Multilayer Perceptron. The method of establishing fraud detection models assists people who lack the machine learning domain expertise for the modeling and tuning parameter techniques. It can help to detect abnormal transactions as early as possible and carry out risk management for banking industry.
KW - Bayesian optimization
KW - Financial statement fraud
KW - Hyperparameter tuning technologies
UR - http://www.scopus.com/inward/record.url?scp=85131123169&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-04819-7_16
DO - 10.1007/978-3-031-04819-7_16
M3 - Conference contribution
AN - SCOPUS:85131123169
SN - 9783031048180
T3 - Lecture Notes in Networks and Systems
SP - 148
EP - 154
BT - Information Systems and Technologies - WorldCIST 2022
A2 - Rocha, Alvaro
A2 - Adeli, Hojjat
A2 - Dzemyda, Gintautas
A2 - Moreira, Fernando
PB - Springer Science and Business Media Deutschland GmbH
T2 - 10th World Conference on Information Systems and Technologies, WorldCIST 2022
Y2 - 12 April 2022 through 14 April 2022
ER -