AIPowered Trust and Security: Enhancing ECommerce with Blockchain and Machine Learning

Nisher Ahmed; Md Emran  Hossain; Zakir  Hossain; Md Farhad  Kabir; Iffat Sania  Hossain

doi:10.55927/fjst.v4i1.13680

Authors

Nisher Ahmed College of Technology and Engineering, Westcliff University
Md Emran Hossain College of Technology and Engineering, Westcliff University
Zakir Hossain College of Engineering and Computer Science, California State University
Md Farhad Kabir Marshall School of Business, University of Southern California
Iffat Sania Hossain Martin V. Smith School of Business and Economics, California State University

DOI:

https://doi.org/10.55927/fjst.v4i1.13680

Keywords:

Blockchain, Machine Learning, Ecommerce Security, Fraud Prevention, Predictive Analytics

Abstract

The ecommerce wave we saw over the years, not just added more opportunities, but added more challenges, especially in the area of trust and security. Fraud, data theft and a lack of transparency remain causes for concern for both businesses and consumers. This paper explores new possibilities of using blockchain and machine learning in designing a robust Artificial Intelligence (AI)based secure ecommerce ecosystem. The immutability of data, transparency, and decentralized control of blockchain act against counterfeit products, payment fraud, and integrity of supply chains. In parallel, machine learning algorithms provides realtime threat detection, predictive analytics, and personalized security measures to detect and counteract threats preemptively. The solution that is proposed leverages the benefits of these technologies to enhance trust among all parties involved, improve operational efficiency, and offer a more secure and trustworthy ecommerce environment. We address the underlying tech stack, realworld application, and next steps in leveraging the convergence of blockchain and ML technologies to transform ecommerce security for a clean and secure digital market.

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