Psycho-Linguistic Fraud Intercept: Predictive Deception Profiling via Real-time Affective Computing on Unstructured Customer Communications

Abstract

Contemporary service sectors face unprecedented challenges from sophisticated fraudulent activities that exploit psychological vulnerabilities through advanced social engineering techniques targeting vulnerable populations during initial communication phases. The Psycho-Linguistic Fraud Intercept (PLFI) framework represents a paradigmatic advancement in proactive risk prevention, leveraging real-time affective computing and natural language processing technologies to identify deceptive intent and vulnerability indicators during initial customer interactions. This advanced system goes beyond traditional keyword detection methods by applying reasoning to detect cognitive and emotional structures in natural language engagements across a broad spectrum of channels, including chat, email, and voice. The PLFI framework implements multi-layered architectures, incorporating semantic, pragmatic, and affective dimensions to facilitate communications through advanced pattern recognition algorithms that identify indicators of linguistic credibility, emotional consistency, and behavior monitoring for anomalies. Dynamic psychological profiling capabilities generate individualized risk assessments that evolve continuously throughout customer interactions, while hybrid detection algorithms combine rule-based methods with machine learning models to identify emerging deception strategies. Real-time intervention mechanisms enable immediate protective responses before harm occurs, implementing automated warning systems and escalation protocols. Comprehensive ethical frameworks address privacy protection, consent management, regulatory compliance, and algorithmic fairness considerations essential for the responsible deployment of psychological profiling technologies in commercial service environments.