FRAUDULENT ORDER DETECTION IN SUPPLY CHAIN TRANSACTIONS USING BEHAVIORAL SIGNALS

Abstract

Supply chain systems process a vast number of transactions daily, making them
highly susceptible to fraudulent activities such as fake orders, unauthorized purchases,
and manipulation of transaction records. Detecting such fraud manually is both timeconsuming
and inefficient due to the large volume and complexity of transactional
data. Therefore, there is a need for an intelligent and automated system to identify and
prevent fraudulent activities effectively.
This project focuses on detecting fraudulent orders in supply chain transactions by
analyzing behavioral signals generated during order processing. These behavioral
signals include user activity patterns, order frequency, transaction timing, location
variations, and purchasing behavior. By analyzing these patterns, the system can
identify deviations from normal behavior, which may indicate potential fraud.
The proposed system utilizes data analysis and machine learning techniques to
monitor and evaluate transaction data in real time. It learns the normal behavioral
patterns of users and compares them with incoming transactions to detect anomalies.
When suspicious activities are identified, the system generates alerts for
administrators, enabling timely intervention to prevent financial losses.
The implementation of this system enhances transparency, improves fraud detection
accuracy, and strengthens the overall security of supply chain operations. This project
demonstrates how behavioral analytics combined with intelligent algorithms can
effectively detect fraudulent activities and support secure and reliable transaction
management in modern supply chains.