arXiv:2410.09066cs.LG2024-10被引 6

生成式AI正催生新型金融犯罪,需发展协同对抗的智能防御体系。

AI versus AI in Financial Crimes and Detection: GenAI Crime Waves to Co-Evolutionary AI

  • 构建双向对抗的共演化AI防御框架,应对生成式AI驱动的犯罪升级。
  • 预计到2027年,生成式AI将使欺诈损失翻至四倍,年增长率超30%。
  • 适合金融机构与网络安全团队关注,尤其重视跨行业协作应对新威胁。

犯罪实体在传统与新兴金融犯罪范式中广泛采用AI,尤其令人担忧的是生成式AI的普及,其已赋能从高级钓鱼攻击、难以识别的深度伪造到生物识别系统欺骗等各类犯罪活动。人工智能被用于犯罪目的的趋势持续加剧,带来前所未有的挑战。AI的应用使欺诈类型日益复杂、交织于网络安全漏洞之中。据估计,生成式AI将在2027年前使欺诈损失增加三倍(即四倍于当前水平),年均增长率超过30%。随着犯罪模式愈发个性化且隐蔽,部署有效的基于AI的防御策略已成为必要。然而,现有检测系统面临多重障碍。本文分析了当前由AI/ML驱动的金融犯罪与检测系统的最新趋势,强调亟需开发敏捷的智能防御机制以应对快速演变的威胁,并呼吁金融服务业加强合作,共同应对生成式AI引发的犯罪浪潮。

原文摘要 · Abstract (English)

Adoption of AI by criminal entities across traditional and emerging financial crime paradigms has been a disturbing recent trend. Particularly concerning is the proliferation of generative AI, which has empowered criminal activities ranging from sophisticated phishing schemes to the creation of hard-to-detect deep fakes, and to advanced spoofing attacks to biometric authentication systems. The exploitation of AI by criminal purposes continues to escalate, presenting an unprecedented challenge. AI adoption causes an increasingly complex landscape of fraud typologies intertwined with cybersecurity vulnerabilities. Overall, GenAI has a transformative effect on financial crimes and fraud. According to some estimates, GenAI will quadruple the fraud losses by 2027 with a staggering annual growth rate of over 30% [27]. As crime patterns become more intricate, personalized, and elusive, deploying effective defensive AI strategies becomes indispensable. However, several challenges hinder the necessary progress of AI-based fincrime detection systems. This paper examines the latest trends in AI/ML-driven financial crimes and detection systems. It underscores the urgent need for developing agile AI defenses that can effectively counteract the rapidly emerging threats. It also aims to highlight the need for cooperation across the financial services industry to tackle the GenAI induced crime waves.

生成式AI金融犯罪智能防御共演化

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