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Online credit card fraud presents a significant challenge in the field of eCommerce. In 2012 alone, the total loss due to credit card fraud in the US amounted to $ 54 billion. Especially online games merchants have difficulties applying standard fraud detection algorithms to achieve timely and accurate detection. This paper describes the Special constrains of this domain and highlights the reasons why conventional algorithms are not quite effective to deal with this problem. Our suggested solution for the problem originates from the fields of feature construction joined with the field of temporal sequence data mining. We present Feature construction techniques, which are able to create discriminative features based on a sequence of transaction and are able to incorporate the time into the classification process. In addition to that, a framework is presented that allows for an automated and adaptive change of features in case the underlying pattern is changing.
The recent years and especially the Internet have changed the ways in which data is stored. It is now common to store data in the form of transactions, together with ist creation time-stamp. These transactions can often be attributed to Logical units, e.g., all transactions that belong to one customer. These groups, we refer to them as data sequences, have a more complex structure than tuple-based data. This makes it more difficult to find discriminatory patterns for classification purposes. However, the complex structure potentially enables us to track behaviour and its change over the course of time. This is quite interesting, especially in the e-commerce area, in which classification of a sequence of customer actions is still a challenging task for data miners. However, before standard algorithms such as Decision Trees, Neural Nets, Naive Bayes or Bayesian Belief Networks can be applied on sequential data, preparations are required in order to capture the information stored within the sequences. Therefore, this work presents a systematic approach on how to reveal sequence patterns among data and how to construct powerful features out of the primitive sequence attributes. This is achieved by sequence aggregation and the incorporation of time dimension into the feature construction step. The proposed algorithm is described in detail and applied on a real-life data set, which demonstrates the ability of the proposed algorithm to boost the classification performance of well-known data mining algorithms for binary classification tasks.
A sequence of transactions represents a complex and multi dimensional type of data. Feature construction can be used to reduce the data´s dimensionality to find behavioural patterns within such sequences. The patterns can be expressed using the blue prints of the constructed relevant features. These blue prints can then be used for real time classification on other sequences.