FRAUD DETECTION IN BANKING DATA BY MACHINE LEARNING TECHNIQUES

Abstract

Fraud detection in banking has become a critical concern due to the increasing number of fraudulent activities affecting financial institutions and customers. Machine learning techniques offer an effective approach to identifying and preventing fraud by analyzing large volumes of transactional data and detecting suspicious patterns. Traditional rule-based fraud detection methods are often limited in adaptability and fail to recognize new and evolving fraudulent behaviors. Machine learning models, such as decision trees, support vector machines, neural networks, and ensemble methods, can enhance fraud detection by learning from historical data and identifying anomalies in real-time transactions.