Work

Credit card fraud detection

Year: 2026

Type: article

Abstract: FraudGuard XGB is a machine learning project that detects credit card fraud using the XGBoost algorithm, motivated by the fact that global fraud losses exceeded $32 billion in 2023. The system processes real transaction data (284,807 records from Kaggle) through a pipeline of preprocessing, SMOTE balancing to handle class imbalance, and XGBoost training, targeting over 99% accuracy. It was compared against Random Forest, Logistic Regression, and SVM, and benchmarked against commercial systems like Stripe Radar and Feedzai — standing out for being open-source, reproducible, and capable of real-time fraud scoring with full feature importance transparency. This work was conducted at Arab International University (AIU), Syria. The official website of the university is: https://www.aiu.edu.sy

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.19536943

Authors: Al Sawaf, Tarek barhoum

Institution: Arab International University

Language: English

Topic: Imbalanced Data Classification Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W7153768067

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