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July 24, 2024World Journal of Advanced Research and Reviews

Utilizing machine learning algorithms to prevent financial fraud and ensure transaction security

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Authors

SOShadrack ObengKPMG (United States)TIToluwalase Vanessa IyeloluAAAdetola Adewale Akinsulire

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Implication

Comprehensive review demonstrates machine learning approaches for financial fraud prevention in banking systems, highlighting technical requirements for robust transaction security.

Key Points

  • Machine learning methods enhance transaction security by identifying fraudulent behaviors, though data quality and real-time processing constraints hinder widespread deployment.
  • Comprehensive overview evaluates supervised learning, neural networks, and anomaly detection algorithms to counter fraudulent activities across complex financial systems.
  • Highlights the necessity of balancing model interpretability with privacy safeguards, which may foster institutional trust and enable resilient transaction security architectures.

Cite This Study

Obeng et al. (2024) studied this question.

synapsesocial.com/papers/68e5f2dcb6db6435875876a0https://doi.org/10.30574/wjarr.2024.23.1.2185
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Machine Learning in Financial Transaction Fraud Detection and Prevention2024 · 35 citations
  2. 2Implementing machine learning algorithms to detect and prevent financial fraud in real-time2024 · 29 citations
  3. 3Online transaction fraud detection in the banking sector using machine learning techniques2024 · 3 citations
  4. 4MACHINE LEARNING APPROACHES FOR FRAUD DETECTION IN FINANCIAL NETWORKS2026
  5. 5Securing Digital Finance: Applying Machine Learning for Fraud Analysis2024 · 3 citations