AI-Driven Fraud Detection Systems: A Comparative Study across Banking, Insurance, and Healthcare
Keywords:
AI, Fraud Detection, Banking, Insurance, Healthcare, Comparative Study, Machine Learning, Anomaly Detection, Data Analytics, Regulatory ComplianceAbstract
Artificial intelligence (AI)-driven fraud detection systems have emerged as crucial tools in safeguarding the integrity of financial, insurance, and healthcare sectors. This paper presents a comprehensive comparative study across these domains, evaluating the efficacy, scalability, and adaptability of AI-based fraud detection mechanisms. Through an analysis of existing literature and empirical data, we examine the diverse approaches employed in detecting fraudulent activities, considering the unique challenges and regulatory frameworks within each sector. Our findings highlight the advancements in machine learning algorithms, anomaly detection techniques, and data analytics driving the evolution of fraud detection systems. We discuss key factors influencing the performance of AI-driven solutions, including data quality, model interpretability, and computational resources. Moreover, this study explores the implications of AI adoption on fraud prevention strategies, organizational risk management, and customer trust. By synthesizing insights from banking, insurance, and healthcare contexts, this research aims to provide valuable guidance for stakeholders seeking to enhance their fraud detection capabilities in an increasingly digitalized landscape.
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Copyright (c) 2023 Pankaj Zanke
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