Analysis of Credit Card Fraud Data Using Various Machine Learning Methods

Chethana, C; Pareek, Piyush Kumar

doi:10.1201/9781003298335-8

Cited by 24 publications

(1 citation statement)

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“…Second, too much reliance on static rules may lead to a large number of false positives, as not all unusual transactions are fraudulent. For example, a consumer may increase the frequency or amount of purchases during the holiday season, and this normal spending behaviour may be incorrectly flagged as fraudulent [9]. Finally, a fraudster may be able to devise strategies to avoid detection by knowing these rules.…”

Section: Traditional Fraud Detection Methodsmentioning

confidence: 99%

Machine Learning in Financial Transaction Fraud Detection and Prevention

Pan

2024

TEBMR

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In this paper, we delve into the application of machine learning technology in financial transaction fraud detection and prevention, including how it works, real-world examples, the challenges it faces, and its potential impact on the future of the financial security space. As the digitalisation process of the financial industry accelerates, the means of financial transaction fraud are becoming increasingly complex and varied, bringing great risks to individuals, enterprises and even the entire financial system. In this context, traditional fraud detection methods are increasingly difficult to effectively respond to emerging frauds due to their inherent limitations. In contrast, machine learning, with its powerful data processing capability, complex pattern recognition ability, and self-learning and adaptation, is considered a powerful tool against financial transaction fraud. Through real-world case studies, we demonstrate how the application of machine learning techniques in financial fraud detection and prevention has helped financial institutions improve detection efficiency and accuracy. Covering a wide range of financial fraud types from credit card fraud to account hijacking to money laundering, these cases illustrate how machine learning models can play a role in monitoring transaction activities in real time, identifying unusual behaviours, and adapting to new fraudulent tactics. In addition, we also discuss the main challenges faced when implementing machine learning techniques, including data quality and privacy protection, model interpretability, the cost of implementing the technology, and integration issues with existing systems. While machine learning shows great potential for financial transaction fraud detection and prevention, there are a number of technical and practical challenges that need to be overcome in order to take full advantage of its benefits. These include improving the efficiency of data collection and processing, ensuring transparency and interpretability of models, reducing the cost of technology implementation and enhancing cross-industry collaboration. In the future, as machine learning and related technologies continue to advance, and as financial institutions and regulators gain a deeper understanding of these technologies, it is reasonable to believe that machine learning will play an even more important role in the financial security field, contributing to the building of a safer, more efficient and smarter financial system. The goal of this paper is to provide financial institutions, technology developers, and policy makers with a comprehensive view of the application of machine learning in financial transaction fraud detection and prevention, pointing out future directions and potential strategies by analysing its advantages, challenges, and practical examples. Through this research, we hope to facilitate broader industry discussions, promote innovation and development of financial security technologies, and jointly address the challenges of financial fraud.

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Section: Traditional Fraud Detection Methodsmentioning

confidence: 99%