Developments in Artificial Intelligence for Petroleum Industry Fraud Detection: An Extensive Analysis and Learnings from Animal Behavior
Keywords:
Regulatory Compliance, Financial Fraud, E-commerce Fraud, Healthcare Fraud, Telecommunications Fraud, Energy Sector Fraud, Animal Behavior, Machine Learning, Ant Colony Optimization (ACO), Swarm Intelligence, Privacy Concerns, Bias and Fairness, Explainable AI (XAI),Abstract
This thorough analysis looks at how animal behavior insights and artificial intelligence (AI) can be combined to improve fraud detection in a variety of sectors. Novel techniques for detecting and thwarting fraudulent actions are created by utilizing cutting-edge AI technologies and taking cues from resilient and adaptable animal behaviors. The study explores the convergence of AI and animal behavior, reviews the historical development of AI in fraud detection, and emphasizes the creation and application of hybrid models. Important case studies from industries including banking, e-commerce, healthcare, telecommunications, and energy show how these integrative solutions can be successfully used in real-world situations. The paper also discusses upcoming technologies, industry-specific innovations, and the significance of ethical considerations as future prospects in AI-driven fraud detection. To guarantee the appropriate use of AI, privacy concerns, bias and fairness, openness and accountability, and regulatory compliance are carefully considered. This work highlights the possibilities and difficulties of this multidisciplinary approach to fraud detection by presenting a multifaceted picture of AI and animal-inspired methods. This will help researchers, practitioners, and policymakers make informed decisions.
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