TY - GEN
T1 - On an ant colony-based approach for business fraud detection
AU - Liu, Ou
AU - Ma, Jian
AU - Poon, Pak Lok
AU - Zhang, Jun
N1 - Funding Information:
This research is supported in part by a departmental general research fund of the Hong Kong Polytechnic University (Project no. G-U442).
PY - 2009
Y1 - 2009
N2 - Nowadays we witness an increasing number of business frauds. To protect investors' interest, a financial firm should possess an effective means to detect such frauds. In this regard, artificial neural networks (ANNs) are widely used for fraud detection. Traditional back-propagation-based algorithms used for training an ANN, however, exhibit the local optima problem, thus reducing the effectiveness of an ANN in detecting frauds. To alleviate the problem, this paper proposes an approach to training an ANN using an ant colony optimization technique, through which the local optima problem can be solved and the effectiveness of an ANN in fraud detection can be improved. Based on our approach, an associated prototype system is designed and implemented, and an exploratory study is performed. The results of the study are encouraging, showing the viability of our proposed approach.
AB - Nowadays we witness an increasing number of business frauds. To protect investors' interest, a financial firm should possess an effective means to detect such frauds. In this regard, artificial neural networks (ANNs) are widely used for fraud detection. Traditional back-propagation-based algorithms used for training an ANN, however, exhibit the local optima problem, thus reducing the effectiveness of an ANN in detecting frauds. To alleviate the problem, this paper proposes an approach to training an ANN using an ant colony optimization technique, through which the local optima problem can be solved and the effectiveness of an ANN in fraud detection can be improved. Based on our approach, an associated prototype system is designed and implemented, and an exploratory study is performed. The results of the study are encouraging, showing the viability of our proposed approach.
KW - Ant colony optimization
KW - Artificial neural network
KW - Fraud detection
UR - http://www.scopus.com/inward/record.url?scp=70350405424&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-04070-2_116
DO - 10.1007/978-3-642-04070-2_116
M3 - Conference Proceeding
AN - SCOPUS:70350405424
SN - 3642040691
SN - 9783642040696
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 1104
EP - 1111
BT - Emerging Intelligent Computing Technology and Applications - 5th International Conference on Intelligent Computing, ICIC 2009, Proceedings
T2 - 5th International Conference on Intelligent Computing, ICIC 2009
Y2 - 16 September 2009 through 19 September 2009
ER -