Approaches to fraud detection on credit card transactions using artificial intelligence methods

In this paper, we study the problem of detecting fraudulent credit card transactions. We select the most relevant features using a heuristic approach, and fit three different model classes to a simulated dataset: Logistic Regression, Random Forests and Gradient Boosting Machines. We find that hyperparameter tuning has a big impact on the precision and recall of our classifiers. We also find that of the three classes, Gradient Boosting Machines were the best-performing model class, achieving 83% precision and 64% recall on unseen data.

In this paper, we study the problem of detecting fraudulent credit card transactions. We select the most relevant features using a heuristic approach, and fit three different model classes to a simulated dataset: Logistic Regression, Random Forests and Gradient Boosting Machines. We find that hyperparameter tuning has a big impact on the precision and recall of our classifiers. We also find that of the three classes, Gradient Boosting Machines were the best-performing model class, achieving 83% precision and 64% recall on unseen data.

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