International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 05 | May 2025
p-ISSN: 2395-0072
www.irjet.net
AI DRIVEN FRAUD DETECTION IN E-COMMERCE PLATFORM Sreelakshmi Jayakuma1, Asnamol Asharaf2, Jelith Nasnin3, Nejma M A4 123
Student , Ilahia College Of Engineering and Technology, Muvattupuzha, Kerala, India
4 Assistant Professor, Ilahia College Of Engineering and Technology, Muvattupuzha, Kerala, India
-------------------------------------------------------------------------------***--------------------------------------------------------------------------Abstract—Create an AI-based fraud detection system To address this challenge, this project proposes an AIdesigned to enhance consumer safety on e-commerce platforms like Instagram and Facebook. With the increasing popularity of online shopping, users are vulnerable to fraudulent accounts and misleading advertisements. The proposed system, which works on both computers and mobile devices, evaluates the authenticity of e-commerce sites and social media profiles by analyzing key indicators such as domain age, trust badges, social media presence, and review consistency. By leveraging machine learning to identify patterns of fraud, the system offers a fraud risk assessment before users finalize purchases, reducing exposure to scams and building trust in online transactions. It integrates natural language processing (NLP) to analyze reviews, product descriptions, and account details for linguistic patterns linked to fraud. Additionally, real-time data processing ensures fast and efficient evaluations, helping users make informed decisions. The system’s intuitive interface provides consumers with trustworthiness scores and warning indicators, contributing to safer online shopping.
Key Words—Fraud Detection, E-commerce Platform, Mobile Devices, Domain age, social media.
1. INTRODUCTION The rapid growth of e-commerce on social media platforms like Instagram and Facebook has revolutionized online shopping, offering convenience and accessibility to millions of users. However, this expansion has also led to a surge in fraudulent activities, with fake accounts and deceptive advertisements promoting counterfeit or low-quality products. These scams exploit the trust of unsuspecting consumers, resulting in financial losses and a decline in confidence in online transactions. Traditional fraud detection methods, such as manual reviews or basic rule-based systems, are no longer sufficient to combat the sophisticated tactics employed by modern fraudsters, necessitating a more advanced and proactive solution. The project aims to create an intuitive and user-friendly interface that seamlessly integrates with social media platforms, allowing users to access trustworthiness scores and warning indicators in real time. The system will continuously learn and adapt to new fraud trends, ensuring its effectiveness in combating evolving fraudulent tactics.
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driven fraud detection system that leverages machine learning, natural language processing (NLP), and real-time data analysis. By analyzing key indicators such as domain age, review consistency, social media presence, and linguistic patterns in product descriptions, the system aims to provide users with accurate and timely fraud risk assessments. This solution not only empowers consumers to make informed purchasing decisions but also fosters a safer and more trustworthy e-commerce environment. By continuously adapting to new fraud trends, the system aspires to enhance consumer protection and support legitimate businesses in the digital marketplace. The increasing popularity of e-commerce on social media platforms like Instagram and Facebook has created a fertile ground for fraudulent activities including take accounts deceptive advertisements, and the promotion of counterfeit or low-quality products. Despite the convenience of online shopping, consumers are often exposed to scams that result in financial losses, poor product quality, and a lack of trust in digital transactions. Current fraud detection methods, such as manual reviews and basic rule- based systems, are inadequate in addressing the scale and sophistication of modern ecommerce fraud. The primary objective of this project is to develop an AI driven fraud detection system that enhances consumer safety and trust in e-commerce transactions on social media platforms like Instagram and Facebook. By leveraging machine learning, natural language processing (NLP), and real-time data analysis, the system aims to identify and assess fraudulent activities, such as fake accounts, deceptive advertisements, and counterfeit product promotions. The system will analyze key authenticity indicators, including domain age, review consistency, social media presence, and linguistic patterns in product descriptions, to provide users with a comprehensive fraud risk assessment. This will empower consumers to make informed purchasing decisions and reduce their exposure to online scams.
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