Abstract
This article proposes a new approach on detection of fraudulent credit operations applying computational intelligence techniques. We use a dataset of historical data of customers from a financial entity and we split it to train a classification and clustering algorithm. We train a radial basis function network to classify clients that commit or not credit fraud. Then, we build a Fuzzy c-means clustering to group data points to create customer profiles. This algorithm has the capacity of grouping the data inside clusters and assigning a degree of membership to the points outside the clusters. Subsequently, the trained classification algorithm is applied to the clusters to provide additional information about customer profiles. We demonstrate good performance for fraudulent credit operations and identification of customer profiles.
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
Bolton, R.J., Hand, D.J.: Unsupervised profiling methods for fraud detection. In: Proceedings of Credit Scoring and Credit Control VII (2001)
Bravo, E., Talavera, A., Serra, M.R.: Prediction and explanation in credit scoring problems. In: Data Analytics Applications in Latin America and Emerging Economies. Auerbach Publications, New York (2017)
Cover, T.M.: Geometrical and statistical properties of systems of linear inequalities with applications in pattern recognition. IEEE Trans. Electron. Comput. 14, 326–334 (1965)
Delamaire, L., Abdou, H., Pointon, J.: Credit card fraud and detection techniques: a review. Banks Bank Syst. 4(2), 57–68 (2009)
Geanakoplos, J., Dubey, P.: Credit cards and inflation. Games Econ. Behav. 70, 325–353 (2010)
Ghosh, S., Reilly, D.L.: Credit card fraud detection with a neural-network. In: Proceedings of the Twenty-Seventh Hawaii International Conference on System Sciences (1994)
Guo, T., Li, G.Y.: Neural data mining for credit card fraud detection. In: 2008 International Conference on Machine Learning and Cybernetics, vol. 7 (2008)
Kirkos, E., Spathis, C., Manolopoulos, Y.: Data mining techniques for the detection of fraudulent financial statements. Expert Syst. Appl. 32(4), 995–1003 (2007)
Lichman, M.: UCI machine learning repository (2013). http://archive.ics.uci.edu/ml
Maes, S., Tuyls, K., Vanschoenwinkel, B.: Credit card fraud detection using Bayesian and neural networks (2015)
Sahin, Y., Duman, E.: Detecting credit card fraud by decision trees and support vector machines. In: Vector Machines, International Multiconference of Engineers and Computer Scientists (2011)
Vaishali, V.: Fraud detection in credit card by clustering approach. Int. J. Comput. Appl. 98(3), 29–32 (2014)
Yang, M.S.: A survey of fuzzy clustering. Math. Comput. Model. 18(11), 1–16 (1993)
Zareapoor, M., Seeja, K.R., Alam, M.A.: Analysis on credit card fraud detection techniques: based on certain design criteria. Int. J. Comput. Appl. 52(3), 35–42 (2012)
Zaslavsky, V., Strizhak, A.: Credit card fraud detection using self-organizing maps. Int. J. Inf. Secur. 18, 48 (2006)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Talavera, A., Cano, L., Paredes, D., Chong, M. (2019). Data Mining Algorithms for Risk Detection in Bank Loans. In: Lossio-Ventura, J., Muñante, D., Alatrista-Salas, H. (eds) Information Management and Big Data. SIMBig 2018. Communications in Computer and Information Science, vol 898. Springer, Cham. https://doi.org/10.1007/978-3-030-11680-4_16
Download citation
DOI: https://doi.org/10.1007/978-3-030-11680-4_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-11679-8
Online ISBN: 978-3-030-11680-4
eBook Packages: Computer ScienceComputer Science (R0)