BANK FRAUD DETECTION USING ADVANCED MACHINE LEARNING TECHNIQUES

The detection of banking fraud is becoming increasingly vital due to the rising frequency of digital transactions. Machine learning (ML) is an essential way for detecting anomalous behavior, as traditional techniques are unable to tackle the ever changing landscape of online threats. This research examines the swift detection of fraud by organizations utilizing machine learning methodologies, including neural networks, decision trees, random forests, and support vector machines, to evaluate extensive datasets. Machine learning (ML) improves precision, reduces false positives, and enables swift response via feature selection, preprocessing, and model performance assessment. Ultimately, the application of machine learning allows banks to protect consumer transactions, maintain vigilance against fraud, and guarantee that banking is both secure and user-friendly.