Abstract
Financial fraud is a growing problem with far-reaching concerns in the financial sector. Online transaction is the basic problem that raises many fraudulent quires around the world which cause loss of money to the people. These transactions generated huge volume of complex data in daily life. The depiction of fraud from credit card is still a key challenge due to two main reasons: firstly, profiles of ordinary and fraudulent behavior changes with the Passage of time, and secondly highly skewed credit card fraud records. Therefore, this study considered this challenge and proposed the solution to identify the fraudulent transactions through the credit cards using data mining techniques. Data mining has played a significant role in identifying credit card fraud from online transactions. Dataset collected from the publically available source and refine it. The employed classifiers are Naive Bayes, Bayes net, Logistic regression, Random forest, Decision tree, support vector machine, Decision stump, K- Nearest Neighbor, J48 and Binary Classification Technique. These techniques are applied on the preprocessed data. This data consists of 284,785 credit card transactions. Extensive experiments were conducted. The accuracy of each classifier was recorded in order to perform comparison. Our empirical analysis spotlights that K-NN outperforms in term of accuracy which is 99.95% than other classifiers. The findings of this study would be useful for the banking sector.
Keywords
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Amanze, B.C., Asogwa, D.C., Chukwuneke, C.I.: Credit card fraud detection system using intelligent agents and enhanced security features. Int. J. Trend Res. Dev. 5(3), 524–530 (2018)
Anushree, B., Kumar, R.: A novel machine learning approach to detect credit card fraud using ECSVM. J. Eng. 8(11), 54–62 (2018)
Bathala, S.B., Nagendra, M., Kandakatla, M.: A review on banking sector. Int. J. Eng. Tech. 3(6), 681–688 (2017)
de Sa, A.G.C., Adriano, C.M., et al.: A customized classification algorithm for credit card fraud detection. Eng. Appl. Artif. Intell. 72, 21–29 (2018)
Jain, R.B.G., Dubey, S.: A hybrid approach for credit card fraud detection using rough set and decision tree technique (2016)
Kaneri, A., Anugrah, S., et al.: Fraud detection in online credit card payment. Int. Res. J. Eng. Technol. 5(3), 2921–2923 (2018)
Khare, N., Sait, S.Y.: Credit card fraud detection using machine learning models and collating machine learning models. Int. J. Pure Appl. Math. 118(20), 825–838 (2018)
Kho, J.R.D., Vea, L.: Credit card fraud detection based on transaction behavior. In: IEEE Region 10 Conference, pp. 1880–884 (2017)
Mekterovic, I., Brkic, K., Baranovic, M.: A systematic review of data mining approaches to credit card fraud detection. WSEAS Trans. Bus. Econ. 15, 437–444 (2018)
Navamani, C., Krishnan, S.: Credit card nearest neighbor based outlier detection techniques. Int. J. Comput. Tech. 5(2), 56–60 (2018)
Sharma, S., Mittal, P., Geetika, G.: An approach to detect credit card frauds using attribute selection and ensemble techniques. Int. J. Comput. Appl. 180(21), 1–6 (2018)
Vimala, S., Sharmili, K.C.: Survey paper for credit card fraud detection using data mining techniques. Int. J. Innov. Res. Sci. Eng. Technol. 6(11), 357–364 (2017)
Acknowledgements
I would like to thank to my mentor Dr. M. Azam Zia to provide necessary support, motivation and infrastructure to carry out the research work. I also want to thank my loving parents for their continuous help and support.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Ul Ain, Q., Zia, M.A., Asghar, N., Saleem, A. (2020). Analysis of Variant Data Mining Methods for Depiction of Fraud. In: Xu, J., Duca, G., Ahmed, S., García Márquez, F., Hajiyev, A. (eds) Proceedings of the Fourteenth International Conference on Management Science and Engineering Management. ICMSEM 2020. Advances in Intelligent Systems and Computing, vol 1190. Springer, Cham. https://doi.org/10.1007/978-3-030-49829-0_31
Download citation
DOI: https://doi.org/10.1007/978-3-030-49829-0_31
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-49828-3
Online ISBN: 978-3-030-49829-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)