MACHINE LEARNING'S EFFECTIVENESS IN BANKING FRAUD DETECTION

Abstrak

Research suggests machine learning (ML) is highly effective for detecting banking fraud, with models like Random Forest and XGBoost achieving accuracies often above 95% in studies, though real-world performance varies due to evolving fraud tactics and data imbalances. It seems likely that ensemble methods outperform single classifiers by better handling anomalies in transaction data, reducing false positives that could disrupt legitimate activities. Evidence leans toward ML improving fraud prevention in areas like credit card transactions, but challenges such as class imbalance and lack of interpretability may limit absolute reliability, especially in diverse banking contexts. Studies indicate supervised learning dominates, with unsupervised approaches useful for novel threats, highlighting a balanced view where ML enhances but does not fully eliminate risks.