A Combinatorial Predictive Method for Fraud Identification to Uphold Security and Data Integrity

Abstract

With the prevalence of digital transactions and customer interactions in today's industry, there has been a significant increase in demand for systems that can detect fraud. Fraud detection is the process of finding and stopping illegal or dishonest activities, usually related to money. Identifying fraudulent behavior accurately and quickly is one of the biggest issues facing the retail industry. This study focuses on the relationship between fraud detection and retail optimization in order to protect profits and improve efficiency. The study suggests a predictive analytics strategy specific to the industry by fusing contextual data, past transaction data, and customer behavior patterns in an innovative way by utilizing machine learning techniques. Retailers can improve inventory management, pricing strategies, and resource allocation by applying the insights gleaned from these models. Because of ethical concerns, this study emphasizes the use of customer data to identify fraudulent activity while maintaining confidentiality and integrity.