International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Credit Card Fraud Detection Using Machine Learning |
|---|---|
| ABSTRACT | Credit card fraud has become a major concern in the modern financial ecosystem due to the rapid growth of digital transactions and online payment systems. Financial institutions face significant losses every year as fraudsters continuously develop sophisticated techniques to exploit system vulnerabilities. Traditional fraud detection methods, which rely on static rule-based approaches, are often inefficient in identifying new and evolving fraud patterns, leading to delayed detection and increased financial risk. This paper presents a machine learning-based credit card fraud detection system that leverages classification algorithms to identify fraudulent transactions in real-time. The proposed system utilizes algorithms such as Logistic Regression, Decision Tree, and Random Forest to analyze transaction data and classify them as legitimate or fraudulent. The dataset used in this study is highly imbalanced, with fraudulent transactions representing a very small portion of the total data. To address this issue, data preprocessing techniques such as normalization and resampling are applied to improve model |
| AUTHOR | MISBA TABASSUM, SHAHEEN M, NAZIYA BANU, MEHEK S, PROF. ARCHANA K N UG Students, Dept. of CSE, Jain Institute of Technology, Davangere, Karnataka, India Assistant Professor, Dept. of CSE, Jain Institute of Technology, Davangere, Karnataka, India |
| VOLUME | 183 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1404084 |
| pdf/84_Credit Card Fraud Detection Using Machine Learning.pdf | |
| KEYWORDS | |
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