INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016
Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
In the rapidly evolving landscape of digital financial transactions, the need for effective fraud detection systems is paramount. This paper presents Fraud Guard, an anomaly detection system designed to identify fraudulent financial transactions from a comprehensive dataset of historical transactions. The system employs a multi-faceted approach that includes data collection, preprocessing, and feature engineering to enhance its detection accuracy. By extracting relevant features from transaction data and considering external data sources such as IP geolocation,
Fraud Guard aims to provide a robust solution to combat
financial fraud.
Keywords:
— Fraud Detection, Machine Learning, Data Mining, Anomaly Detection, Feature Engineering, Supervised Learning, Unsupervised Learning, Deep Learning, Big Data, Financial Fraud, Credit Card Fraud, Fraud Prevention, Fraud Detection Algorithms
Cite Article:
"Fraud Detection: Anomaly Detection System for Financial Transactions", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.8, Issue 11, page no.c472-c477, November-2023, Available :http://www.ijnrd.org/papers/IJNRD2311260.pdf
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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
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