An Intelligent Method for Fraud Detection in Digital Payments based on SVR with GC-RF Approach

The use of automated algorithms to detect fraud on electronic payment networks is challenging. Digital payment systems and their users are vulnerable to cybercriminals who take advantage of security holes or users' negligence to steal passwords, perpetrate fraud, launder money, and carry out other malicious acts. Conventional methods of fraud detection are challenging to execute because of the difficulties of acquiring massive volumes of manually annotated data. It is tough to notice new trends because fraudsters are often changing their techniques. Feature extraction, model training, and data preprocessing were the main areas of emphasis in this systematic research. Data pretreatment encompassed tasks such as acquiring training sample data, cleaning, converting, integrating, and altering the data. Feature extraction is the backbone of SVR-GC-RF model training; it takes all the data in a dataset and turns it into features. The suggested method outperformed SVR and RF in terms of accuracy by 95.23 percent. The importance of hierarchical fraud detection in online payment systems is highlighted in this paper. Through the use of effective feature extraction and model training, the study enhances fraud detection. Methods for detecting fraud need to change if they are to keep up with the criminals.