The problem of preprocessing transaction data for supervised fraud classification is considered. It is impractical to present an entire series of transactions to a fraud detection system, partly because of the very high dimensionality of such data but also because of the heterogeneity of the transactions. Hence, a framework for transaction aggregation is considered and its effectiveness is evaluated against transaction-level detection, using a variety of classification methods and a realistic cost-based performance measure. These methods are applied in two case studies using real data. Transaction aggregation is found to be advantageous in many but not all circumstances. Also, the length of the aggregation period has a large impact upon performance. Aggregation seems particularly effective when a random forest is used for classification. Moreover, random forests were found to perform better than other classification methods, including SVMs, logistic regression and KNN. Aggregation also has the advantage of not requiring precisely labeled data and may be more robust to the effects of population drift.
Peer group analysis is an unsupervised method for monitoring behaviour over time. In the context of plastic card fraud detection, this technique can be used to find anomalous transactions. These are transactions that deviate strongly from their peer group and are flagged as potentially fraudulent. Time alignment, the quality of the peer groups and the timeliness of assigning fraud flags to transactions are described. We demonstrate the ability to detect fraud using peer groups with real credit card transaction data and define a novel method for evaluating performance.
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