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Revolutionizing Financial Fraud Detection and Risk Management
Abstract
This paper explores the revolutionary impact of AI-driven engineering on financial fraud detection and risk management. Through case studies and research insights, it investigates how artificial intelligence is transforming traditional approaches to fraud prevention, detection, and mitigation in the financial sector. The study highlights the application of AI techniques such as anomaly detection, pattern recognition, and predictive analytics in identifying fraudulent activities, detecting suspicious patterns, and assessing risk levels in real-time. Additionally, it discusses the integration of AI with big data analytics, blockchain technology, and cybersecurity measures to enhance the security and resilience of financial systems. The paper also addresses challenges such as data privacy, regulatory compliance, and adversarial attacks in the deployment of AI-driven engineering solutions in financial fraud detection and risk management. It emphasizes the importance of collaboration between financial institutions, technology providers, and regulatory authorities to harness AI's potential in safeguarding financial assets and maintaining trust in the financial system.
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References
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