发表机构
Federal University of Technology Minna; University of Michigan-Dearborn; Clemson University（米纳联邦理工大学; 密歇根大学迪尔伯恩分校; 克莱姆森大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对尼日利亚金融科技中AI部署前评估缺乏监管规范的问题，本文提出监管框架，并通过SafeAlert评估工具证明现有安全基准的不足。
AI 中文摘要
商业大型语言模型正越来越多地部署在非洲金融科技基础设施中，用于欺诈检测和客户沟通，然而，没有任何尼日利亚或非洲大陆的监管文书明确规定这些系统在采购前必须经过何种部署前评估。本文回顾了全球、非洲大陆及尼日利亚层面的非洲金融科技AI治理框架，并表明安全虽被确立为一项原则，但部署前评估在操作层面未作具体规定。通用安全基准无法揭示与该领域最相关的失败模式，因为其中既不包含尼日利亚机构内容，也不测试对合法金融通信的误报错误分类。这些主张通过SafeAlert（一个专门构建的评估工具包）得到验证，该工具包应用于六个商业模型，在三种系统提示条件下进行测试。结果表明，能够抵御通用有害内容请求的模型在特定框架下仍会生成完整的欺诈脚本，且多个模型将大多数合法的尼日利亚银行通信误分类为可疑或欺诈，这一失败在标准安全评估中不可见。本文最后提出一个监管框架，为CBN、NITDA、SEC和AU建议部署前评估要求，并论证所识别的差距反映了监管规范的缺失，而非技术或财政资源的短缺。
英文摘要
Commercial large language models are increasingly deployed across African fintech infrastructure for fraud detection and customer communication, yet no Nigerian or African continental regulatory instrument specifies what pre-deployment evaluation such systems must undergo before procurement. This paper reviews African fintech AI governance across global, continental, and Nigerian instruments, and shows that safety is affirmed as a principle while pre-deployment evaluation is operationally unspecified. Generic safety benchmarks cannot surface the failure modes most relevant to this domain, since none contain Nigerian institutional content or test for false positive misclassification of legitimate financial communications. These claims are demonstrated using SafeAlert, a purpose-built evaluation kit applied to six commercial models across three system prompt conditions. Results show that models resisting generic harmful content requests still produce complete fraud scripts under specific framing, and that several models misclassify most legitimate Nigerian bank communications as suspicious or fraudulent, a failure invisible to standard safety evaluation. The paper concludes with a regulatory framework proposing pre-deployment evaluation requirements for the CBN, NITDA, SEC, and the AU, arguing that the identified gap reflects an absence of regulatory specification, not a shortage of technical or financial resources.
