Real-Time Credit Card Fraud Detection using deep Learning based framework

It is one of the most important features in financial security that must be done to prevent fraudulent users and malicious agents from swiping credit cards. The extensive use of credit cards for online transactions has legitimized payment fraud. Traditional fraud detection methods: One of the reasons conventional fraud detectors are rendered obsolete is that newer variants of malicious tools and techniques evolve faster than they can be detected. This paper proposes a deep learning approach for real-time credit card fraud detection to resolve this problem. The framework is built using deep learning, which helps to accurately and quickly detect fraud in your real-time data. The system uses advanced deep learning algorithms to analyze real-time transactional data and detect anomalies. Being trained in a data-driven method, the deep learning-based framework can adapt when encountering new fraud patterns. For this reason, it can identify anomalies in fraudulent activities with high accuracy. It also has real-time analytics for large data volumes, so it is suited to financial organizations with a high volume of transactions. Among other pros, this architecture allows far higher throughput for a given latency than traditional approaches. It could also lower the time to respond to fraud detection and potentially reduce financial loss from fraudulent acts. In summary, real-time credit card fraud detection via a deep learning-based framework is one of the most promising ways to secure financial transactions and guard consumers against being affected by this or any other type of online threats.