Credit Card Fraud Detection in Banking Using Machine Learning

Credit card fraud has become a main issue in banking, causing big losses and diminishing trust among customers. Conventional methods of detection fail to catch up with new tactics used by fraudsters. The use of machine learning strategies in credit card fraud detection has been discussed here. We apply complex algorithms such as logistic regression, decision trees, random forests, and neural networks to analyze transaction data in order to identify patterns characteristic of fraud patterns. The study focuses on the feature engineering, model estimation, and real- time detection mechanisms to achieve improved accuracy and reduce false positives. Experimental results demonstrate that machine learning-based techniques surpass traditional methods and offer an excellent solution to avoid fraud risk. This research emphasizes the potential of machine learning to revolutionize fraud detection in banking for enhanced security and customer satisfaction.