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METAFRAUD: AN ANALYSIS OF MULTIDIMENSIONAL APPROACH TO FRAUD DETECTION
Authors
Hemashree H,Anushka G,Harshitha H,Bhavana DK,Dr . Suma T
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Keywords
Credit card fraud detection, Hybrid system, Machine learning algorithms Rule-based systems, Fraud detection accuracy, Financial industry, Supervised machine learning, Unsupervised techniques, Anomaly detection, Dataset pre-processing, Algorithms like Random Forest , Decision tree , Evaluation metrics, False-positive rate, Financial losses, Novel fraud patterns, Advanced feature engineering, Real-time fraud detection, Deep learning algorithms, Data sources, IP addresses, Device fingerprints, Data
Abstract
Credit card fraud poses a significant challenge within the financial industry, intensifying with the surge in credit card usage. This predicament not only results in financial losses for individuals and businesses but also erodes trust in the broader financial system. As a countermeasure, an imperative lies in the improvement of a robust fraud detection system. This paper introduces an innovative approach, leveraging algorithms of machine learning to pinpoint fraudulent transactions. By enhancing accuracy and expediting the identification process, our proposed system aims to fortify the resilience against credit card fraud.
The escalating threat of the credit card fraud, particularly fuelled by the proliferation of online transactions and vulnerability of sensitive information, necessitates advanced detection methods. Various strategies, including rule-based systems, Algorithms like machine learning and neural networks, have been adopted to combat this menace. In response to this evolving landscape, our paper suggests a hybrid system that amalgamates rule-based and the machine learning approaches. This integration seeks to elevate the precision of fraud detection. Through rigorous evaluation using a comprehensive dataset, our hybrid system demonstrates superior performance note the similarity between existing methodologies.Recognizing the enduring challenges posed by fraudsters and their continual adaptation to circumvent existing systems, our hybrid model is designed to address the dynamic nature of credit card fraud. Incorporating a set of rules to identify common fraud patterns and a algorithms of machine learning to discern intricate anomalies, our system achieves noteworthy results--boasting an accuracy of 99.86%, a precision of 99.68%, and a recall of 81.30%. With a focus on reducing false positive and adeptly detecting both simple and complex fraud patterns, our proposed system stands as a potent defence mechanism.
This article not only adds to the ongoing discourse on credit card fraud but also holds
implications for the broader financial industry. By mitigating the risks associated with fraud, our approach strives to safeguard the financial interests of individuals and businesses alike. As we delve into the future scope of the research paper, the potential for further advancements in the credit card fraud detection becomes apparent, signalling a pivotal step towards a more secure financial landscape.
IJCRT's Publication Details
Unique Identification Number - IJCRT2407850
Paper ID - 265338
Author type - Indian Author
Page Number(s) - h669-h677
Pubished in - Volume 12 | Issue 7 | July 2024
DOI (Digital Object Identifier) -
No Of Downloads - 168
Author Country - India, 560064, Bangalore , Bangalore , 560064, Science and Technology
Publisher Name - IJPUBLICATION | www.ijcrt.org | ISSN : 2320-2882
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Hemashree H,Anushka G,Harshitha H,Bhavana DK,Dr . Suma T, "METAFRAUD: AN ANALYSIS OF MULTIDIMENSIONAL APPROACH TO FRAUD DETECTION", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.12, Issue 7, pp.h669-h677, July 2024, Available at :http://www.ijcrt.org/papers/IJCRT2407850.pdf
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