Fraud detection in capital markets: A novel machine learning approach
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Fraud detection in capital markets : A novel machine learning approach. / Yi, Ziwei; Cao, Xinwei; Pu, Xujin; Wu, Yiding; Chen, Zuyan; Khan, Ameer Tamoor; Francis, Adam; Li, Shuai.
In: Expert Systems with Applications, Vol. 231, 120760, 2023.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Fraud detection in capital markets
T2 - A novel machine learning approach
AU - Yi, Ziwei
AU - Cao, Xinwei
AU - Pu, Xujin
AU - Wu, Yiding
AU - Chen, Zuyan
AU - Khan, Ameer Tamoor
AU - Francis, Adam
AU - Li, Shuai
N1 - Publisher Copyright: © 2023 Elsevier Ltd
PY - 2023
Y1 - 2023
N2 - Traditional auditing methods require collating massive amounts of financial indicators and related transaction data, which can be labor-intensive. Typical machine learning models are relatively weak for imbalanced data, and this work aims to focus on a novel approach to fraud detection. This paper presents a fraud detection framework via adopting a machine learning method integrated with a recently proposed meta-heuristics algorithm Egret Swarm Optimization Algorithm (ESOA). A cost-sensitive objective function and loss function were then constructed, and a non-linear model was used to map the predicted values into the labels of 0 (non-fraud) and 1 (fraud). In the experiment section, an AAER benchmark dataset collected by the UCB's Center for Financial Reporting and Management is utilized to verify the performance of the proposed approach. A detailed comparison with recently proposed state-of-the-art algorithms such as Logit (67.20%), SVM-FK (62.60%), RUSBoost (72.60%), as well as BAS (84.90%) indicates that ESOA (96.27%) outperforms the other algorithms in terms of Accuracy (ACC), Sensitivity (SEN), Precision (PREC), and Area Under the Curve (AUC) metrics. To our knowledge, this is the highest fraud detection accuracy reported in the existing literature.
AB - Traditional auditing methods require collating massive amounts of financial indicators and related transaction data, which can be labor-intensive. Typical machine learning models are relatively weak for imbalanced data, and this work aims to focus on a novel approach to fraud detection. This paper presents a fraud detection framework via adopting a machine learning method integrated with a recently proposed meta-heuristics algorithm Egret Swarm Optimization Algorithm (ESOA). A cost-sensitive objective function and loss function were then constructed, and a non-linear model was used to map the predicted values into the labels of 0 (non-fraud) and 1 (fraud). In the experiment section, an AAER benchmark dataset collected by the UCB's Center for Financial Reporting and Management is utilized to verify the performance of the proposed approach. A detailed comparison with recently proposed state-of-the-art algorithms such as Logit (67.20%), SVM-FK (62.60%), RUSBoost (72.60%), as well as BAS (84.90%) indicates that ESOA (96.27%) outperforms the other algorithms in terms of Accuracy (ACC), Sensitivity (SEN), Precision (PREC), and Area Under the Curve (AUC) metrics. To our knowledge, this is the highest fraud detection accuracy reported in the existing literature.
KW - Egret Swarm Optimization Algorithm
KW - ESOA
KW - Fraud detection
KW - Listed corporates
KW - Machine learning
U2 - 10.1016/j.eswa.2023.120760
DO - 10.1016/j.eswa.2023.120760
M3 - Journal article
AN - SCOPUS:85162084094
VL - 231
JO - Expert Systems with Applications
JF - Expert Systems with Applications
SN - 0957-4174
M1 - 120760
ER -
ID: 360825566