A Hybrid Simulated Annealing and Back-propagation Algorithm for Feed-forward Neural Network to Detect Credit Card Fraud

  • Ardalan Husin Awlla Ministry of Education, Sulaimani 46001, Iraq

Abstract

Due to the ascent and fast development of E-commerce, utilization of credit cards for online buys has significantly expanded, and it brought about a blast in the credit card fraud. As credit card turns into the most prevalent method of installment for both online and also normal buy, cases of fraud associated with it are additionally rising. In actuality, false exchanges are scattered with veritable exchanges, and basic example for coordinating procedures is not frequently adequate to identify those frauds accurately. Usage of effective fraud recognition frameworks has in this manner gotten to be basic for all credit card distributing banks to decrease their losses. Many current systems based on artificial intelligence, Fuzzy logic, machine learning, data mining, sequence alignment, genetic programming, and so on have advanced in distinguishing different credit card fake transactions. A reasonable seeing on all these methodologies will absolutely lead to an efficient credit card fraud detection framework. This paper suggested an anomaly detection model based on a hybrid simulated annealing (SA) and back-propagation algorithm for feed-forward neural network (FFNN), which joined the significant global searching capability of SA with the precise local searching element of back-propagation FFNNs to improve theinitial weights of a neural network toward getting a better result for detection fraud.


Index Terms: Artificial Neural Network, Back-propagation, Back-propagation Feed-forward Neural Network, Feed-forward Neural Network, Simulated Annealing, Simulated Annealing-back-propagation Feed-forward Neural Network

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Published
2017-08-29
How to Cite
AWLLA, Ardalan Husin. A Hybrid Simulated Annealing and Back-propagation Algorithm for Feed-forward Neural Network to Detect Credit Card Fraud. UHD Journal of Science and Technology, [S.l.], v. 1, n. 2, p. 31-36, aug. 2017. ISSN 2521-4217. Available at: <http://journals.uhd.edu.iq/index.php/uhdjst/article/view/15>. Date accessed: 24 nov. 2017. doi: https://doi.org/10.21928/uhdjst.v1n2y2017.pp31-36.
Section
Articles