Improving Credit Card Fraud Detection using a Meta-learning Strategy

One of the issues facing credit card fraud detection systems is that a significant percentage of transactions labeled as fraudulent are in fact legitimate. These “false alarms” delay the detection of fraudulent transactions. Analysis of 11 months of credit card transaction data from a major Canadian bank was conducted to determine savings improvements that can be achieved by identifying truly fraudulent transactions. A meta-classifier model was used in this research. This model consists of 3 base classifiers constructed using the k-nearest neighbour, decision tree, and naïve Bayesian algorithms. The naïve Bayesian algorithm was also used as the meta-level algorithm to combine the base classifier predictions to produce the final classifier. Results from this research show that when a meta-classifier was deployed in series with the Bank’s existing fraud detection algorithm a 24% to 34% performance improvement was achieved resulting in $1.8 to $2.6 million cost savings per year.

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