A Study on the Role of Machine Learning Techniques in Modelling and Fraud Detection in E-bidding Systems

Abstract

E-bidding has become a crucial element in modern procurement and auction systems, ensuring transparency, efficiency, and competitiveness. The integration of machine learning (ML) into E-bidding processes enhances bid evaluation, fraud detection, and decision-making. This paper explores the application of ML techniques such as predictive modelling, anomaly detection, and natural language processing (NLP) to optimize bidding strategies, assess bidder credibility, and prevent fraudulent activities. By leveraging ML algorithms, E-bidding systems can automate complex evaluations, ensuring optimal outcomes for buyers and sellers. The study highlights the impact of AI-driven decision-making in E-bidding and discusses future advancements in this domain. Machine learning has revolutionized various aspects of e-bidding processes and systems, enhancing efficiency, fairness, and decision-making. This response explores the applications of machine learning across different dimensions of e-bidding, including auction design, personalized bidding strategies, real-time bidding, and multi-agent systems. The discussion is supported by insights from relevant research papers, highlighting the advancements and innovations in this field.