International Journal For Multidisciplinary Research

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AI-Based Fraud Detection System in Online Transactions

Author(s) Ms. Tirangini Singh Negi, Ms. Varsha Chaurasiya, Ms. Priyanshi Trivedi, Ms. Upma Mishra
Country India
Abstract In the fast-paced digital world, the habit of online transactions has become the part and parcel of everyday life. On the other hand, it has also increased the fraudulent factor regarding cybercrime. Artificial Intelligence, in this regard, may help to a great extent in order to detect fraud and prevent it in real time. This research is aimed at the development of an AI-based fraud detection system with an ability to analyze patterns of transactions for the detection of suspicious behavior in order to minimize financial loss. The system learns from previous transaction data using machine learning algorithms to predict whether a new incoming transaction is genuine or fraudulent. Techniques such as data preprocessing, extraction of features, and classification models are combined in the proposed system for effective anomaly direction with accuracy. This study will enhance the security and trustworthiness of online payment systems to help banks, e-commerce platforms, and users to protect their financial information.
Online financial transactions have grown as a crucial part of the digital economy; at the same time, they are facing fraudulent activities related to phishing, identity theft, and payment manipulation. These kinds of threats are complicated and continuously evolving; hence, it is impossible for traditional systems based on rules to handle them. It is here that AI has proven to be a game-changing solution with the incorporation of ML, DL, and other anomaly detection models. The goal is to facilitate adaptive, data-driven fraud detection. This paper is an attempt to unify very recent works and present a holistic perspective on AI-driven fraud detection in online transactions by summarizing the current methodologies, models, datasets, challenges, and future directions.
Field Computer Applications
Published In Volume 7, Issue 6, November-December 2025
Published On 2025-11-27
DOI https://doi.org/10.36948/ijfmr.2025.v07i06.60742
Short DOI https://doi.org/hbdrdm
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