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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Issue: May 2026
Volume 11 | Issue 5
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Impact Factor : 8.14
Issue per Year : 12
Volume Published : 11
Issue Published : 120
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Total Authors : 24597
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| Paper Title: | Development of a Real -Time Fraud Detection System for Online Financial Transactions using Supervised Machine learning |
| Authors Name: | Sowbarnika G , Arthi P , Dhanalakshmi V , Hareethaa S , Rithika V K |
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IJRTI_212057
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| Published Paper Id: | IJRTI2604321 |
| Published In: | Volume 11 Issue 4, April-2026 |
| DOI: | |
| Abstract: | The rapid growth of digital payment systems, including online banking, e-commerce platforms, and mobile wallets, has increased both transaction speed and the risk of fraudulent activity. Traditional fraud detection techniques, which rely mainly on fixed rules and predefined thresholds, are often unable to detect evolving and sophisticated fraud patterns in real time. To address this issue, this study presents a machine learning-based system for identifying fraud in online financial transactions. The proposed system uses supervised learning models to analyze transaction attributes and classify them as legitimate or suspicious by examining features such as amount, location, device information, and time-related behavior. Since fraud datasets are usually highly imbalanced, SMOTE is applied to generate synthetic minority samples and improve model learning. Several classifiers, including Logistic Regression, Random Forest, and XGBoost, are evaluated using performance measures such as accuracy, precision, recall, and F1-score. The best-performing model is integrated into a Flask-based web application that provides real-time fraud prediction through an interactive dashboard. An audio alert feature is also included to give immediate notification of suspicious transactions. The experimental results show improved fraud detection performance, higher accuracy, and fewer false alerts compared to conventional rule-based methods. These findings demonstrate that supervised machine learning can play an important role in strengthening the security of digital financial transactions. |
| Keywords: | Fraud Detection, Machine Learning, Financial Transactions, SMOTE, Real-Time Prediction, Random Forest. |
| Cite Article: | "Development of a Real -Time Fraud Detection System for Online Financial Transactions using Supervised Machine learning", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 4, page no.c378-c383, April-2026, Available :http://www.ijrti.org/papers/IJRTI2604321.pdf |
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2604321
Registration ID:212057
Published In: Volume 11 Issue 4, April-2026
DOI (Digital Object Identifier):
Page No: c378-c383 Country: Tiruvannamalai, Tamil Nadu, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2604321 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2604321 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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