Abstract
Banking industry is one of the most complex and sensitive industries that experience enormous changes in daily basis. Likemany others businesses, Big data is a serious problematic, data management and real time monitoring fraud issues also are even bigger challenges in this sector, due to the huge quantity of data, coming swiftly and rapidly from different devices in structured and unstructured formats, waiting for instantaneously treatments and decisions. Most financial institutions and banks try to innovate and diversify payment processes to make it more challenging and secure to improve their digital skills. Understand customer’s behaviors also become a successful key factor in the market at the same time, that’s why Internet of Things (IoT) can be the best solution to solve the issue of collecting and sharing data via internet among different “things”, as devices and objects (Sensors, ATMs, POS, Smartphones, Computers, payment gateways (ecommerce), notebooks, etc.). The architectural and technical sides remain a problem, since conventional database management system and existing banking systems are not capable anymore to handle, store and process this massive volume of data with sufficient real time. This paper, discuss Hadoop Distributed File System and MapReduce, as an architecture for storing and retrieving information from massive volumes of datasets that we can collect via Internet from different objects based on the advantage and potential of Internet of things.
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Van Vlasselaer, V., Bravo, C., Caelen, O., Eliassi-Rad, T., Akoglu, L., Snoeck, M., Baesens, B.: APATE: A novel approach for automated credit card transaction fraud detection using network-based extensions. Decis. Support Syst. 75, 38–48 (2015)
Mahmoudi, N., Duman, E.: Detecting credit card fraud by modified fisher discriminant analysis. Expert Syst. Appl. 42(5), 2510–2516 (2015)
Halvaiee, N.S., Akbari, M.K.: A novel model for credit card fraud detection using artificial immune systems. Appl. Soft Comput. 24, 40–49 (2014)
West, J., Bhattacharya, M.: Payment card fraud detection using neural network committee and clustering. Comput. Secur. 57, 47–66 (2016)
Zareapoor, M., Shamsolmoali, P.: Application of credit card fraud detection: Based on bagging ensemble classifier. Procedia Comput. Sci. 48, 679–685 (2015)
Duman, E., Buyukkaya, A., Elikucuk, I.: A novel and successful credit card fraud detection system implemented in a Turkish bank. In: 2013 IEEE 13th International Conference on Data Mining Workshops (ICDMW). IEEE (2013)
Bahnsen, A.C., et al.: Cost sensitive credit card fraud detection using Bayes minimum risk. In: 2013 12th International Conference on Machine Learning and Applications (ICMLA), Vol. 1. IEEE (2013)
Wei, W., et al.: Effective detection of sophisticated online banking fraud on extremely imbalanced data. World Wide Web 16(4), 449–475 (2013)
Hormozi, E., et al.: Accuracy evaluation of a credit card fraud detection system on Hadoop MapReduce. In: 2013 5th Conference on Information and Knowledge Technology (IKT). IEEE (2013)
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Boumlik, A., Bahaj, M. (2018). Big Data and IoT: A Prime Opportunity for Banking Industry. In: Ezziyyani, M., Bahaj, M., Khoukhi, F. (eds) Advanced Information Technology, Services and Systems. AIT2S 2017. Lecture Notes in Networks and Systems, vol 25. Springer, Cham. https://doi.org/10.1007/978-3-319-69137-4_35
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DOI: https://doi.org/10.1007/978-3-319-69137-4_35
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