Abstract

The surge in digital financial services has created new vulnerabilities to fraud, requiring advanced detection systems. Conventional fraud identification methods struggle with real-time processing, particularly when analyzing severely imbalanced datasets. This study introduces a multi-faceted AI framework combining tree-based boosting algorithms (LightGBM, XGBoost, CatBoost) with neural computation to improve fraud identification. Utilizing the creditcard.csv dataset containing 284,807 transactions where only 0.17% represents fraudulent activities, 1, 16 specialized techniques were implemented rebalancing approaches and parameter optimization to enhance detection performance. Results demonstrate that tree-based boosting approaches excel in precision metrics, lowering false alerts, while neural computation achieves superior sensitivity and discrimination capability 3, 4, 5. Specifically, XGBoost reached 88.17% precision with 97.25% area under curve, 4 CatBoost maintained balanced performance indicators, 5 and the neural architecture delivered 82.65% sensitivity with 97.95% discrimination capability 49. These outcomes illustrate how computational intelligence enhances financial security protocols, reducing unauthorized activities and minimizing institutional risk exposure.

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