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 | Financial Fraud Detection Application |
|---|---|
| ABSTRACT | Financial fraud poses a persistent and escalating threat to global economic stability, resulting in substantial annual losses for individuals and financial institutions alike. Traditional, manual, and rules-based detection methods are often inefficient, time-consuming, and incapable of adapting to the increasing sophistication and volume of modern fraudulent activities. This study explores the application and effectiveness of advanced data analytics and machine learning (ML) techniques in automating and enhancing the accuracy of financial fraud detection systems across various domains, including credit card transactions, insurance claims, and banking operations. We analyze several supervised and unsupervised ML algorithms, such as Logistic Regression, Random Forests, Support Vector Machines (SVM), Artificial Neural Networks (ANN), and advanced deep learning models like Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. |
| AUTHOR | PURVA YOGESH PAWAR, SHRUTI VAIBHAV KHARCHE, RITA SANJAY MAHAJAN, PROF. VIKAS NARKHEDE UG Student, Dept. of Computer Engineering, KCE's College of Engineering and Management, Jalgaon, India Assistant Professor, Dept. of Computer Engineering, KCE's College of Engineering and Management, Jalgaon, India |
| VOLUME | 184 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1405079 |
| pdf/79_Financial Fraud Detection Application.pdf | |
| KEYWORDS | |
| References | [1] A. Srivastava, A. Kundu, S. Sural, A. Majumdar, “Credit Card Fraud Detection Using Hidden Markov Model”, IEEE Transactions on Dependable and Secure Com puting, Vol. 5, Issue 1, ISSN: 1545-5971, pp. 37-48, Jan 2008. [2] I. Witten, E. Frank, M. Hall, “Data Mining Techniques” in Data Mining: Practical Machine Learning Tools and Techniques, 3rd Edition, Volume 1. Morgan Kaufmann, USA: Elsevier, 2011, pp. 45-60. [3] V. Bhusari, S. Patil, “Study of Machine Learning Algorithms for Financial Fraud De tection”, International Journal of Computer Applications, Vol. 182, Issue 45, ISSN: 0975-8887, pp. 20-25, Mar 2019. [4] A. Srivastava, A. Kundu, S. Sural, A. Majumdar, “Credit Card Fraud Detection Using Hidden Markov Model”, IEEE Transactions on Dependable and Secure Com puting, Vol. 5, Issue 1, ISSN: 1545-5971, pp. 37-48, Jan 2008. [5] I. H. Witten, E. Frank, M. A. Hall, “Data Mining Techniques” in Data Mining: Practical Machine Learning Tools and Techniques, 3rd Edition, Volume 1. Morgan Kaufmann, USA: Elsevier, 2011, pp. 45-60. |
