The impact of changing populations on classifier performance
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Machine Learning
Data Mining and Knowledge Discovery
Distributed Data Mining in Credit Card Fraud Detection
IEEE Intelligent Systems
Neural Data Mining for Credit Card Fraud Detection
ICTAI '99 Proceedings of the 11th IEEE International Conference on Tools with Artificial Intelligence
Neural fraud detection in credit card operations
IEEE Transactions on Neural Networks
A hybrid model for plastic card fraud detection systems
Expert Systems with Applications: An International Journal
Mining data with random forests: A survey and results of new tests
Pattern Recognition
Data mining for credit card fraud: A comparative study
Decision Support Systems
Detecting fraud in online games of chance and lotteries
Expert Systems with Applications: An International Journal
Outlier analysis for plastic card fraud detection a hybridized and multi-objective approach
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part II
PB-ADVISOR: A private banking multi-investment portfolio advisor
Information Sciences: an International Journal
Employing transaction aggregation strategy to detect credit card fraud
Expert Systems with Applications: An International Journal
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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.