Neural Networks for Fraud Detection in Financial Transactions
Abstract:
Fraudulent activities in financial transactions are a major concern for financial institutions and individuals. Machine learning techniques have shown promising results in detecting fraudulent transactions. In this paper, we propose the use of neural networks for fraud detection in financial transactions. We develop a deep learning model that takes into account the time-series nature of financial transactions and the inherent imbalanced nature of fraud detection. The proposed model uses a combination of convolutional neural networks and long short-term memory networks to extract features from the transaction data and make predictions about the likelihood of fraud. We evaluate the performance of the proposed model on a publicly available dataset and compare it with other state-of-the-art machine learning techniques. Our results show that the proposed model outperforms existing methods, achieving a higher area under the receiver operating characteristic curve and F1 score.
"Neural Networks for Fraud Detection in Financial Transactions", IJNRD - INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (www.IJNRD.org), ISSN:2456-4184, Vol.8, Issue 3, page no.e300-e301, March-2023, Available :https://ijnrd.org/papers/IJNRD2303438.pdf
Volume 8
Issue 3,
March-2023
Pages : e300-e301
Paper Reg. ID: IJNRD_188604
Published Paper Id: IJNRD2303438
Downloads: 000118851
Research Area: Engineering
Country: Thane, Maharashtra, India
ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJNRD (IJ Publication) Janvi Wave
