Abstract
Fraudulent activities associated with the credit card is a pertinent problem often occurring in a global level. The customers are losing their trust with the financial institutions and the financial institutions are in a difficult state to win the goodwill of customers. A substantial number of researchers show interest to work on fraud detection in order to develop an optimized method or model to identify the fraudulent activities that are happening in a regular and continuous form with the credit card in our everyday life. Genetic algorithm (GA) and the potential solution-based particle swarm optimization (PSO) are two optimization algorithms, which can be considered along with the neural network to analyze the possible fraudulent transactions. The optimization algorithms help to make the learning process faster and optimized with a superior and better predictive accuracy value. The PSO-based neural network has been trained thoroughly and performance values are compared with GA-based neural network, by increasing the number of iterations and the population or number of swarms. It has been observed that algorithm based on PSO gives an optimized result for fraudulent transaction detection.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Behdad M, Barone L, Bennamoun M, French T (2012) Nature-inspired techniques in the context of fraud detection. IEEE Trans Syst Man Cybern Part C (Appl Rev) 42(6):1273–1290
Gayathri C, Umarani R (2015) Efficient detection of financial fraud detection by selecting optimal ensemble architecture using optimization approaches. Indian J Innov Dev 4(8):1–9
Gadi MFA, Wang X, do Lago AP (2008) Credit card fraud detection with artificial immune system. In: International conference on artificial immune systems, pp 119–131. Springer, Berlin
Prusti D (2015) Efficient intrusion detection model using ensemble methods. PhD dissertation
Prusti D, Harshini Padmanabhuni SS, Rath SK (2020) Credit card fraud detection by implementing machine learning techniques. In: Safety, security, and reliability of robotic systems. CRC Press, Boca Raton, pp 205–216
Duman E, Hamdi Ozcelik M (2011) Detecting credit card fraud by genetic algorithm and scatter search. Exp Syst Appl 38(10):13057–13063
Alam S, Dobbie G, Riddle P, Asif Naeem M (2010) A swarm intelligence based clustering approach for outlier detection. In: IEEE congress on evolutionary computation, pp 1–7. IEEE
Shahreza ML, Moazzami D, Moshiri B, Delavar MR (2011) Anomaly detection using a self-organizing map and particle swarm optimization. Scientia Iranica 18(6):1460–1468
Panigrahi S, Kundu A, Sural S, Majumdar AK (2009) Credit card fraud detection: a fusion approach using Dempster–Shafer theory and Bayesian learning. Inf Fus 10(4):354-363
Sánchez D, Vila MA, Cerda L, Serrano JM (2009) Association rules applied to credit card fraud detection. Expert Syst Appl 36(2):3630–3640
Mitchell M (1998) An introduction to genetic algorithms. MIT press
Aote SS, Raghuwanshi MM, Malik L (2013) A brief review on particle swarm optimization: limitations & future directions. Int J Comput Sci Eng (IJCSE), 14(1):196–200
Bratton D, Kennedy J (2007) Defining a standard for particle swarm optimization. In 2007 IEEE swarm intelligence symposium, IEEE, pp 120–127
Brause R, Langsdorf T, Hepp M (1999) Neural data mining for credit card fraud detection. In: Proceedings 11th international conference on tools with artificial intelligence, pp 103–106. IEEE
Prusti D, Rath SK (2019) Fraudulent transaction detection in credit card by applying ensemble machine learning techniques. In: 2019 10th international conference on computing, communication and networking technologies (ICCCNT), pp 1–6. IEEE
Ghosh S, Reilly DL (1994) Credit card fraud detection with a neural-network. In: Proceedings of the twenty-seventh Hawaii international conference on system sciences, vol 3, 621–630. IEEE
Patidar R, Sharma L (2011) Credit card fraud detection using neural network. Int J Soft Comput Eng (IJSCE) 1:32–38
Zhang Y, Wang S, Lenan W, Huo Y (2010) PSONN used for remote-sensing image classification. J Comput Inform Syst 6(13):4417–4425
Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks, Piscataway, NJ, pp 39–43
https://shodhganga.inflibnet.ac.in/bitstream/10603/181389/12/12_chapter%204.pdf, Particle swarm optimization
ElÃas A, Ochoa-Zezzatti A, Padilla A, Ponce J (2011) Outlier analysis for plastic card fraud detection a hybridized and multi-objective approach. In: International conference on hybrid artificial intelligence systems, pp 1–9. Springer, Berlin
Das M, Taylan, Canan Dulger L (2007) Off-line signature verification with PSO-NN algorithm. In: 2007 22nd international symposium on computer and information sciences, pp 1–6. IEEE
Zhang C, Shao H, Li Y (2000) Particle swarm optimization for evolving artificial neural network. In: Proceedings of the IEEE international conference on systems, man and cybernetics 2000, pp 2487–2490
Zhang JR, Zhang J, Lok TM, Lyu MR (2006) A Hybrid particle swarm optimization-back propagation algorithm for feedforward neural network training. Appl Math Comput 185:1026–1037
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Prusti, D., Rout, J.K., rath, S.K. (2023). Detection of Credit Card Fraud by Applying Genetic Algorithm and Particle Swarm Optimization. In: Doriya, R., Soni, B., Shukla, A., Gao, XZ. (eds) Machine Learning, Image Processing, Network Security and Data Sciences. Lecture Notes in Electrical Engineering, vol 946. Springer, Singapore. https://doi.org/10.1007/978-981-19-5868-7_27
Download citation
DOI: https://doi.org/10.1007/978-981-19-5868-7_27
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-5867-0
Online ISBN: 978-981-19-5868-7
eBook Packages: Computer ScienceComputer Science (R0)