Identifying Fraud Detection Techniques Using Text Analytics Processing

Abstract

To determine the fraud detection model, to illustrate how the fraud detection model is created, and to start the data model with any classifier, data mining technology is used in the fraud detection process. As e-commerce continues to grow, the associated internet hoax is still a very appealing source of cash for scammers. Because of the severe financial damage that this counterfeit activity does to retailers, online fraud detection is essential. Concerned with scam detection is the need to quickly seize fraudulent actions in addition to containing them. This significance is essential to reducing financial losses. Cybercrimes are a widespread annoyance and have a negative impact on our society in many ways. In every nation, the police enforcement system heavily relies on the results of cybercrime investigations. The connected online hoax is still a highly alluring way for con artists to get money as e-commerce expands. Online fraud detection is crucial due to this counterfeit activity's significant financial harm to merchants. In addition to curbing fraudulent acts, scam identification raises the urgent need to seize them. To minimize financial losses, its relevance is crucial. Cybercrimes are a common irritant that harms our society in a variety of ways. The outcomes of cybercrime investigations substantially influence the police enforcement systems in every country. These methods are essentially utilized for fraud detection in a variety of industries, including health, insurance, online shopping, and more.