AI AND THE DETECTION OF DECEPTIVE SPEECH AND FACIAL EXPRESSIONS IN FRAUDULENT BEHAVIOR

Abstract

Deception is a critical element in fraudulent activities, which often leads to psychological stress known as cognitive dissonance. This research explores the connection between deception and cognitive dissonance, examining how the mental discomfort associated with deceit presents itself in vocal irregularities. By reviewing psychological studies on cognitive dissonance and its impact on facial expressions and speech patterns, such as pitch variations, speech hesitations, and changes in vocal tone, this study aims to establish a link between deception and measurable vocal cues and expressions. It also examines the potential of artificial intelligence (AI) to detect these irregularities and identify deception. By reviewing AI-driven speech analysis tools and their ability to recognize deceptive speech patterns, this research aims to assess their effectiveness in fraud detection. The findings will contribute to the development of more advanced AI-driven fraud prevention systems by demonstrating how vocal changes linked to deception can serve as reliable indicators of fraudulent behavior.