Enhancing E-Commerce Fraud Detection Using AI-Driven Cybersecurity Systems

E-commerce has grown a lot, and with that, fraud has too. Cybercriminals are getting smarter, and the usual tools we use to stop them aren’t enough anymore. This paper looks at how AI might help fix that. It goes into how AI—things like machine learning, deep learning, and natural language processing—is being used to catch odd behavior, spot risky transactions, and even guess what might go wrong before it does. The paper also looks at how these AI methods hold up against older approaches and points out where they actually work better. But it’s not all smooth. There are problems, like keeping user data private, understanding how AI makes choices, and dealing with fraud tactics that keep changing. The paper also shares a layered AI setup and some real examples from big e-commerce companies. In short, AI seems to be helping. These systems can react faster and adjust as new types of fraud show up. And with online shopping only getting bigger, tools like this are becoming kind of necessary.

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Publication Details

Journal
International Research Journal on Advanced Engineering and Management (IRJAEM)
Published
2025-06-27
DOI
https://doi.org/10.47392/irjaem.2025.0369
Primary Topic
Imbalanced Data Classification Techniques
Type
article
Field-Weighted Citation Impact
0.00
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article

Enhancing E-Commerce Fraud Detection Using AI-Driven Cybersecurity Systems

Dilip Prakash Valanarasu
International Research Journal on Advanced Engineering and Management (IRJAEM)
Imbalanced Data Classification Techniques
article

Enhancing E-Commerce Fraud Detection Using AI-Driven Cybersecurity Systems

Dilip Prakash Valanarasu
article en

Abstract

E-commerce has grown a lot, and with that, fraud has too. Cybercriminals are getting smarter, and the usual tools we use to stop them aren’t enough anymore. This paper looks at how AI might help fix that. It goes into how AI—things like machine learning, deep learning, and natural language processing—is being used to catch odd behavior, spot risky transactions, and even guess what might go wrong before it does. The paper also looks at how these AI methods hold up against older approaches and points out where they actually work better. But it’s not all smooth. There are problems, like keeping user data private, understanding how AI makes choices, and dealing with fraud tactics that keep changing. The paper also shares a layered AI setup and some real examples from big e-commerce companies. In short, AI seems to be helping. These systems can react faster and adjust as new types of fraud show up. And with online shopping only getting bigger, tools like this are becoming kind of necessary.

International Research Journal on Advanced Engineering and Management (IRJAEM)Vol. 3(06)
Alagappa University (IN)
Openalex Percentile: Top 93%
Imbalanced Data Classification Techniques
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