Authors: Abraham Itzhak Weinberg, Alessio Faccia

Published on: March 27, 2024

Impact Score: 7.4

Arxiv code: Arxiv:2403.18322

Summary

• What is new: Exploration of Quantum Machine Learning (QML) and Quantum Artificial Intelligence (QAI) in combating financial crimes, showcasing their superiority over traditional and current ML techniques.
• Why this is important: The rapid spread and complexity of financial crimes, challenging the effectiveness of traditional and ML techniques in combatting them.
• What the research proposes: Utilization of quantum algorithms, specifically QML and QAI, to improve detection and prevention of financial crimes, enhancing financial risk management analysis.
• Results: Quantum approaches significantly overcome limitations of classical methods, offering robust solutions for financial crime detection and risk management.

Technical Details

Technological frameworks used: Quantum computing frameworks leveraging QML and QAI for advanced financial crime detection and risk management.

Models used: Quantum algorithms designed to outperform traditional and ML techniques in detecting financial crimes.

Data used: nan

Potential Impact

Financial institutions, cybersecurity firms, and blockchain-based companies could greatly benefit; traditional cybersecurity and financial services risk being disrupted.

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