A Cloud-Based Intelligent Machine Learning Framework for Real-Time Fraud Detection in Online Transactions

Registration ID: IJNRD_326229

Published ID: IJNRD2606216

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Dhairya Dev, Gaurav kumar, Deepak gupta

fraud detection; machine learning; cloud computing; XGBoost; Isolation Forest; real-time analytics; financial security; anomaly detection

Every time someone taps their phone to pay for coffee, or types a card number into an online checkout, that transaction gets checked — usually in under a second — by a system designed to catch fraud. Most of those systems, if you look under the hood, still rely on rules written by humans: block transactions above a certain amount, flag cards used in two countries too quickly, and so on. It works, to a degree. But fraudsters figure out the rules eventually, and then they work around them. This paper describes an attempt to do something better. We built a fraud detection framework that uses machine learning instead of fixed rules, runs in real time on cloud infrastructure, and combines multiple detection approaches so that no single point of failure can let fraud slip through. The models we used — Logistic Regression, Random Forest, XGBoost, and an unsupervised Isolation Forest — were trained on a publicly released dataset of over 590,000 card transactions originally compiled for an IEEE competition on Kaggle. The short version of the results: XGBoost scored 98.1% accuracy and an AUC-ROC of 0.986. Against a rule-based baseline on the same data, recall improved by 18.4 percentage points, meaning far fewer fraudulent transactions slipped through undetected. Average scoring time came down to about 18 milliseconds per transaction, which is well within the window card networks allow for an authorization decision. On top of that, the Isolation Forest caught another 4.2% of fraud that none of the supervised models had flagged — mostly transactions that looked unusual in ways the training data had not captured before. None of this is a complete solution to fraud. Fraud changes, and any static system eventually falls behind. But a cloud-hosted framework that retrains periodically and scales automatically to handle transaction spikes is, in our view, a much stronger foundation than a rule set that someone last updated six months ago.

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Dhairya Dev, Gaurav kumar, & Deepak gupta (June-2026). A Cloud-Based Intelligent Machine Learning Framework for Real-Time Fraud Detection in Online Transactions. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 11(6), c147-c152. https://ijnrd.org/papers/IJNRD2606216.pdf

Volume 11 Issue 6, June-2026

Pages : c147-c152

Paper Reg. ID: IJNRD_326229

Published Paper Id: IJNRD2606216

Research Area: Other area not in list

Author Type: Indian Author

Country: Meerut , Uttarpardesh , India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2606216.pdf

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Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

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