Reducing Customer Reject Rates through Policy Optimization in Fraud Prevention

Abstract

Customer reject rates in fraud prevention systems often present a significant challenge for businesses, especially in industries such as finance, e-commerce, and telecommunications. These rejection rates, which occur when legitimate transactions or customers are incorrectly flagged as fraudulent, can result in customer dissatisfaction, lost revenue, and damage to brand reputation. This paper explores the relationship between policy optimization and the reduction of customer reject rates within fraud prevention systems. By examining the impact of machine learning algorithms, decision trees, and risk assessment models, the study aims to optimize the decision-making processes that determine whether a transaction or customer is legitimate or fraudulent. Additionally, it discusses the role of policy adjustments, such as the refinement of fraud detection thresholds, the integration of historical data, and the real-time monitoring of transactions,