  In an increasingly digitalized commerce landscape, the proliferation of
credit card fraud and the evolution of sophisticated fraudulent techniques have
led to substantial financial losses. Automating credit card fraud detection is
a viable way to accelerate detection, reducing response times and minimizing
potential financial losses. However, addressing this challenge is complicated
by the highly imbalanced nature of the datasets, where genuine transactions
vastly outnumber fraudulent ones. Furthermore, the high number of dimensions
within the feature set gives rise to the ``curse of dimensionality". In this
paper, we investigate subspace learning-based approaches centered on One-Class
Classification (OCC) algorithms, which excel in handling imbalanced data
distributions and possess the capability to anticipate and counter the
transactions carried out by yet-to-be-invented fraud techniques. The study
highlights the potential of subspace learning-based OCC algorithms by
investigating the limitations of current fraud detection strategies and the
specific challenges of credit card fraud detection. These algorithms integrate
subspace learning into the data description; hence, the models transform the
data into a lower-dimensional subspace optimized for OCC. Through rigorous
experimentation and analysis, the study validated that the proposed approach
helps tackle the curse of dimensionality and the imbalanced nature of credit
card data for automatic fraud detection to mitigate financial losses caused by
fraudulent activities.
