Credit card fraud detection and mitigation with machine learning techniques

The digitization of financial transactions have popularized the escalating sophistication of credit card related cybercrimes. The difficulty in automatically identifying, tracking, and mitigating credit card frauds necessitates equally sophisticated frameworks for credit card fraud detection and mitigation. Recent detection systems have moved beyond static rule-based detection techniques to dynamic machine learning and deep learning algorithms. They have capability to analyze vast streams of transactional data in real or near real-time, using features like unusual geographic spending and atypical purchase sequences as signals to identify non-linear patterns and anomalous behaviors that constitute fraudulent card use. Current credit card fraud mitigation strategies like immediate transaction blocking, card suspension, and multi-factor authentication protocols are outcomes of machine learning research using scarce real-world data. Further research is producing adaptive detection and mitigation algorithms that continuously learn from new fraud attempts, creating a feedback loop that enhances detection accuracy and digital financial security. This comparison identifies some recent works in the domain, highlighting the machine learning methods and techniques used in credit card fraud detection and mitigation.