Comparing AI-Driven Fraud Detection Systems with Traditional Methods

Abstract

The comparison of traditional methods with AI-driven fraud detection systems demonstrates that each approach has its own distinct strengths and limitations. AI systems are extremely effective in modern, data-rich environments due to their advanced analytical capabilities and adaptability. Conversely, conventional methodologies establish a strong foundation of domain knowledge and established practices. Organizations can achieve a more robust and reliable fraud detection framework that is capable of addressing the sophisticated tactics of modern fraudsters by integrating these approaches, thereby leveraging the best of both worlds. Research has demonstrated that the overall efficacy of fraud detection systems is improved by the integration of AI and conventional methods. By automating and refining the rules employed in conventional systems, AI-driven systems can decrease the number of false positives, thereby facilitating more precise and efficient fraud detection. The main aim of this research is to analyze the effects of AI-driven fraud detection systems with traditional methods. For the sake of this 85 respondents from 04 chosen commercial enterprises in Ahmedabad has been chosen. The current study employs percentage analysis and Chi Square test to examine the hypothesis. Findings suggested that the integrated approach has the potential to enhance user trust and confidence by combining the sophisticated capabilities of AI with the reliability of traditional methods.