  Spurious credit card transactions are a significant source of financial
losses and urge the development of accurate fraud detection algorithms. In this
paper, we use machine learning strategies for such an aim. First, we apply a
mixed learning technique that uses K-means preprocessing before trained
classification to the problem at hand. Next, we introduce an adapted detector
ensemble technique that uses OR-logic algorithm aggregation to enhance the
detection rate. Then, both strategies are deployed in tandem in numerical
simulations using real-world transactions data. We observed from simulation
results that the proposed methods diminished computational cost and enhanced
performance concerning state-of-the-art techniques.
