  Card transaction fraud is a growing problem affecting card holders worldwide.
Financial institutions increasingly rely upon data-driven methods for
developing fraud detection systems, which are able to automatically detect and
block fraudulent transactions. From a machine learning perspective, the task of
detecting fraudulent transactions is a binary classification problem.
Classification models are commonly trained and evaluated in terms of
statistical performance measures, such as likelihood and AUC, respectively.
These measures, however, do not take into account the actual business
objective, which is to minimize the financial losses due to fraud. Fraud
detection is to be acknowledged as an instance-dependent cost-sensitive
classification problem, where the costs due to misclassification vary between
instances, and requiring adapted approaches for learning a classification
model. In this article, an instance-dependent threshold is derived, based on
the instance-dependent cost matrix for transfer fraud detection, that allows
for making the optimal cost-based decision for each transaction. Two novel
classifiers are presented, based on lasso-regularized logistic regression and
gradient tree boosting, which directly minimize the proposed instance-dependent
cost measure when learning a classification model. The proposed methods are
implemented in the R packages cslogit and csboost, and compared against
state-of-the-art methods on a publicly available data set from the machine
learning competition website Kaggle and a proprietary card transaction data
set. The results of the experiments highlight the potential of reducing fraud
losses by adopting the proposed methods.
