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 | Literature Survey on Machine Learning Techniques for Fraud Detection |
|---|---|
| ABSTRACT | Fraud detection is a significant issue in the financial industry due to the increase in the adoption of digital payment systems, online banking, and e-commerce sites. Various fraudulent practices, including credit card fraud and illegal financial transactions, result in substantial financial losses for banks and customers. Conventional fraud detection systems are based on rule-based approaches, which are not effective in identifying new and complex fraud patterns. To address these limitations, machine learning is used for fraud detection purposes. Machine learning techniques are used for analyzing the huge volume of transactions and identifying the underlying patterns that indicate illegal and fraudulent activities. Various machine learning algorithms, including Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbor, AdaBoost, XGBoost, ANN, and GNN, are used for fraud detection purposes. This literature survey discusses various machine learning techniques used in fraud detection by analyzing various research papers. The paper compares different techniques, advantages, and disadvantages of various algorithms used in different research papers. The study also identifies various research gaps, including imbalanced datasets, changing patterns of fraud, and computational complexities. The findings of this paper will provide knowledge about various techniques of fraud detection, including the need for efficient machine learning techniques in future financial security systems. |
| TITLE | |
| AUTHOR | M. KRISHNA KUMAR, P. MEENADHARSHINI, S.VARSHA, S.G.ABARNA, R.PRABHU Assistant Professor, Department of Mathematics, AAA College of Engineering and Technology, Sivakasi, Tamil Nadu, India Assistant Professor, Department of Artificial Intelligence and Data Science, AAA College of Engineering and Technology, Sivakasi, Tamil Nadu, India UG Student Department of Artificial Intelligence and Data Science, AAA College of Engineering and Technology, Sivakasi, Tamil Nadu, India |
| VOLUME | 182 |
| DOI | DOI: 10.15680/IJIRCCE. 2026.1403088 |
| pdf/88_Literature Survey on Machine Learning Techniques for Fraud Detection.pdf | |
| KEYWORDS | |
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