针对AI增强型诈骗的有效干预措施

中文摘要 AI 辅助

据估计，2025年全球诈骗造成的直接损失达4420亿美元；美国报告的损失在2020至2025年间增长了近400%。尽管AI用于诈骗是相对较新的现象，但它的使用显著改变了诈骗的经济逻辑及运营瓶颈。本文研究在这种AI驱动的新型诈骗模式下哪些干预措施仍能有效。作者构建了一个简单的诈骗利润模型，以理解不同干预措施如何渐近影响诈骗运营，发现三个杠杆——举报率、举报集中化程度和举报准确率——对每个诈骗渠道的预期受害者数量的影响是相乘的，这会减少每个诈骗渠道的收入，同时增加成本。由于影响是相乘的，同时作用于这三个方面的干预措施可能对诈骗商业模式的盈利能力产生重大影响。分析表明，即使是针对高价值诈骗基础设施的适度举报率，也可能对诈骗盈利能力产生显著影响。

英文摘要

In 2025, scams were responsible for an estimated $442 billion in direct losses globally. In the United States, reported losses increased by nearly 400% between 2020 and 2025. Though AI in scamming is a relatively new phenomenon, its use significantly changes the economics of scams as well as the bottlenecks in scam operations. In this paper I investigate what interventions will remain effective under this new AI-driven scamming regime. I develop a simple model of scam profits to understand how different interventions asymptotically affect scam operations. I find that three levers--reporting rate, centralization of reporting, and report accuracy--multiply in their effect on expected victims per scam channel, reducing revenue per scam channel while increasing costs. Because effects multiply, interventions affecting all three could have a significant effect on the profitability of the scam business model. My analysis suggests that even modest reporting rates against high-value scam infrastructure could have significant impacts on scam profitability.

发表机构

• Quarry Intelligence

机构由 AI 辅助整理，请以论文原文为准。