Fraudulent activities are rapidly evolving, employing increasingly diverse and sophisticated methods that pose serious threats to individuals, organizations, and society. This paper proposes the FIST Framework (Fraud Incident Structured Threat Framework), an innovative structured threat modeling methodology specifically designed for fraud scenarios. Inspired by MITRE ATT\&CK and DISARM, FIST systematically incorporates social engineering tactics, stage-based behavioral decomposition, and detailed attack technique mapping into a reusable knowledge base. FIST aims to enhance the efficiency of fraud detection and the standardization of threat intelligence sharing, promoting collaboration and a unified language across organizations and sectors. The framework integrates interdisciplinary insights from cybersecurity, criminology, and behavioral science, addressing both technical vectors and psychological manipulation mechanisms in fraud. This approach enables fine-grained analysis of fraud incidents, supporting automated detection, quantitative risk assessment, and standardized incident reporting. The effectiveness of the framework is further validated through real-world case studies, demonstrating its value in bridging academic research and practical applications, and laying the foundation for an intelligence-driven anti-fraud ecosystem. To the best of our knowledge, FIST is the first systematic, open-source fraud threat modeling framework that unifies both technical and psychological aspects, and is made freely available to foster collaboration between academia and industry.