Despite living in the era of the internet, phone-based scams remain one of the most prevalent forms of scams. These scams aim to exploit victims for financial gain, causing both monetary losses and psychological distress. While governments, industries, and academia have actively introduced various countermeasures, scammers also continue to evolve their tactics, making phone scams a persistent threat. To combat these increasingly sophisticated scams, detection technologies must also advance. In this work, we propose a framework for modeling scam calls and introduce an LLM-based real-time detection approach, which assesses fraudulent intent in conversations, further providing immediate warnings to users to mitigate harm. Through experiments, we evaluate the method's performance and analyze key factors influencing its effectiveness. This analysis enables us to refine the method to improve precision while exploring the trade-off between recall and timeliness, paving the way for future directions in this critical area of research.

翻译：尽管身处互联网时代，电话诈骗仍是当前最普遍的诈骗形式之一。这类诈骗以非法牟利为目的，不仅造成受害者的财产损失，更带来心理创伤。尽管政府、产业界和学术界已积极推出多种防范措施，诈骗分子亦不断升级其作案手法，使得电话诈骗持续构成现实威胁。为应对日益复杂的诈骗手段，检测技术必须同步发展。本研究提出一种诈骗电话建模框架，并引入基于LLM的实时检测方法，该方法通过评估对话中的欺诈意图，进一步为用户提供即时预警以降低损害。通过实验，我们评估了该方法的性能表现，并分析了影响其效能的关键因素。基于此分析，我们优化了该方法以提升检测精度，同时探索召回率与时效性之间的权衡关系，为这一关键研究领域的未来发展开辟道路。