Fraud Detection in Financial Institutions: AI VS. Traditional Methods

Abstract-This research paper provides a comparative analysis of traditional rule-based fraud detection methods and emerging AI-based approaches in financial institutions. The study examines effectiveness, adaptability, operational efficiency, regulatory compliance, and implementation considerations of both methodologies. Through detailed evaluation supported by visual representations, the paper demonstrates that while AI-based methods offer superior detection accuracy, adaptability, and reduced false positives, traditional approaches provide greater transparency and established regulatory compliance frameworks. The findings suggest that hybrid approaches combining the strengths of both methodologies represent the optimal strategy for most financial institutions. The paper concludes with an examination of future trends and recommendations for financial institutions seeking to enhance their fraud detection capabilities.