Online financial transactions bring convenience to people's lives, but also present vulnerabilities for criminals to embezzle users' accounts and trick users into credit card fraud. Although machine learning methods have been adopted to detect anomalous transactions, it's hard for a single machine learning method to achieve satisfying results with the increasing scale and dimensionality of financial datasets. In addition, for anomaly detection of financial data, there is an obvious imbalance between normal records and abnormal.
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