Salvatore J. Stolfo, David W. Fan, Wenke Lee, Andreas L. Prodromidis  Home/Search
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Abstract: In this paper we describe initial experiments using meta-learning techniques to learn models of fraudulent credit card transactions. Our collaborators, some of the nation's largest banks, have provided us with real-world credit card transaction data from which models may be computed to distinguish fraudulent transactions from legitimate ones, a problem growing in importance. Our experiments reported here are the first step towards a better understanding of the advantages and limitations of... (Update)
Context of citations to this paper: More
.... S v do calculate PfLK = i g (Eq[13] calculate (x) and its standard deviation ( x) Eq[7] estimate accuracy aK (Eq[8] and Eq[9]) and remaining training time mK (Eq[15] if aK and mK satisfy stopping criteria then return C 1 ; C k ; k k 1; Algorithm 1:...
...of cases belonging to each class is common. For instance, in detection of fraud in telephone calls [7] and credit card transactions [15], the number of legitimate transactions is much higher than the number of fraudulent transactions. In insurance risk modelling [12] only...
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BibTeX entry: (Update)
S. Stolfo, W. Fan, W. Lee, A. Prodromidis, and P. Chan. Credit card fraud detection using meta-learning: Issues and initial results. Working notes of AAAI Workshop on AI Approaches to Fraud Detection and Risk Management, 1997. http://citeseer.ist.psu.edu/stolfo97credit.html More
@misc{ stolfo97credit,
author = "S. Stolfo and W. Fan and W. Lee and A. Prodromidis and P. Chan",
title = "Credit card fraud detection using meta-learning: Issues and initial results",
text = "S. Stolfo, W. Fan, W. Lee, A. Prodromidis, and P. Chan. Credit card fraud
detection using meta-learning: Issues and initial results. Working notes
of AAAI Workshop on AI Approaches to Fraud Detection and Risk Management,
1997.",
year = "1997",
url = "citeseer.ist.psu.edu/stolfo97credit.html" }
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