Real-Time Fraud Detection in Financial Transactions Using Autoencoders

The research detailed in this paper describes a fraud detection system where autoencoders are employed in order to detect anomaly in financial transactions in real time manner. The results of experiment analysis in the system are, in the stage of detecting the query object, the detection accuracy of the system reaches 98%. 5%, precision of 92. 3%, recall of 90. accuracy of 95. 1%, a precision of 92. 8%, a recall of 100%, and an F1-score of 91. It must have had a definite impact, as attributable solely to the study, since its efficacy was proven in a real word with the improvement of patients’ health by 2%. Through analysing successive transactional data and learning the new modes of fraudulent activities, the system optimizes the number of both false positives and negatives for effective antifraud measures. The proposed approach can identify fraud in real time, which is critically important to maintaining financial fraud prevention efficiency.