Context-Aware Pre-Deployment Evaluation of AI Systems: A Regulatory Framework for Nigerian Fintech

2026-09-20

AI Summary

本文针对尼日利亚金融科技中AI系统的预部署评估问题，通过构建SafeAlert评测工具，揭示现有安全评估不足，并提出监管框架以弥补规范空白。

Abstract

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.

Problem

• pre-deployment evaluation
• fintech AI governance
• false positive misclassification
• Nigerian institutional content
• safety benchmarks

Innovation

• Context-Aware
• Pre-Deployment Evaluation
• Fintech AI Governance
• Nigerian Institutional Content
• False Positive Misclassification