| Reference : Improving Credit Card Fraud Detection with Calibrated Probabilities |
| Scientific congresses, symposiums and conference proceedings : Paper published in a book | |||
| Engineering, computing & technology : Computer science | |||
| http://hdl.handle.net/10993/15233 | |||
| Improving Credit Card Fraud Detection with Calibrated Probabilities | |
| English | |
Correa Bahnsen, Alejandro  [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >] | |
| Stojanovic, Aleksandar [> >] | |
| Aouada, Djamila [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >] | |
| Ottersten, Björn [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >] | |
| 2014 | |
| Proceedings of the fourteenth SIAM International Conference on Data Mining, Philadelphia, Pennsylvania, USA, April 24-26, 2014. | |
| Society for Industrial and Applied Mathematics | |
| 677-685 | |
| Yes | |
| No | |
| International | |
| 9781611973372 | |
| Philadelphia | |
| USA | |
| 2014 SIAM International Conference on Data Mining | |
| from 24-04-2014 to 26-04-2014 | |
| SIAM Society for Industrial and Applied Mathematics | |
| Philadelphia | |
| United States | |
| [en] Previous analysis has shown that applying Bayes minimum risk to detect credit card fraud leads to better results measured by monetary savings, as compared with traditional methodologies. Nevertheless, this approach requires good probability estimates that not only separate well between positive and negative examples, but also assess the real probability of the event. Unfortunately, not all classification algorithms satisfy this restriction. In this paper, two different methods for calibrating probabilities are evaluated and analyzed in the context of credit card fraud detection, with the objective of finding the model that minimizes the real losses due to fraud. Even though under-sampling is often used in the context of classification with unbalanced
datasets, it is shown that when probabilistic models are used to make decisions based on minimizing risk, using the full dataset provides significantly better results. In order to test the algorithms, a real dataset provided by a large European card processing company is used. It is shown that by calibrating the probabilities and then using Bayes minimum Risk the losses due to fraud are reduced. Furthermore, because of the good overall results, the aforementioned card processing company is currently incorporating the methodology proposed in this paper into their fraud detection system. Finally, the methodology has been tested on a different application, namely, direct marketing. | |
| University of Luxembourg: High Performance Computing - ULHPC | |
| http://hdl.handle.net/10993/15233 |
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