3 résultats pour « Fraud Detection »

Differentiable Inductive Logic Programming for Fraud Detection

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Explainable AI (XAI) is becoming increasingly important, especially in fields like fraud detection. Differentiable Inductive Logic Programming (DILP) is an XAI method that can be used for this purpose. While DILP has scalability issues, data curation can make it more applicable. While it might not outperform traditional methods in terms of processing speed, it can provide comparable results. DILP's potential lies in its ability to learn recursive rules, which can be beneficial in certain use cases.
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Crowdfunding Fraud Detection: A Systematic Review Highlights AI and Blockchain using Topic Modeling

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“… this research provides valuable insights into the complexity of detecting and preventing fraudulent activities in crowdfunding and highlights effective detection techniques that, if implemented, offer promising solutions to enhance platform reputation and ensure regulatory compliance.”
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QFNN‑FFD: Quantum Federated Neural Network for Financial Fraud Detection

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“Our findings reveal that the QFNN-FFD framework, supported by a robust computational infrastructure and optimized through sophisticated preprocessing techniques, can effectively identify fraudulent transactions with high precision. Its resilience against various quantum noise models is particularly noteworthy, indicating its suitability for real-world application in the near-term QC landscape.”
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