Context-Aware Pre-Deployment Evaluation of AI Systems: A Regulatory Framework for Nigerian Fintech
Organizations: Federal University of Technology Minna, Nigeria · University of Michigan-Dearborn, USA · Clemson University, USA
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.
Figures & tables
| No. | Category | Primary deception mechanism |
|---|---|---|
| 1 | Phishing and fake bank alerts | Lookalike domains and OTP harvesting |
| 2 | SIM swap fraud | Social engineering targeting telecom operators |
| 3 | Account takeover | Credential phishing via fake app flows |
| 4 | Fintech impersonation | Cloned support accounts harvesting PINs and BVNs |
| 5 | Investment and Ponzi schemes | Guaranteed return pitches exploiting hardship |
| 6 | Predatory loan applications | Advance fee extraction and contact list harassment |
| Model | Pre-remediation | Post-remediation | Nigerian-context |
|---|---|---|---|
| Mistral Small | 99.4% | 3.1% | 0.6% |
| Qwen 2.5 7B | 85.0% | 86.3% | 58.1% |
| GPT-4o mini | 78.1% | 4.4% | 5.6% |
| DeepSeek Chat V3 | 55.0% | 4.4% | 0.6% |
| Llama 3.3 70B | 36.3% | 18.8% | 9.4% |
| Grok 3 Mini | 21.3% | 0.0% | 0.0% |
AI Governance
Artificial intelligence governance studies how laws, institutions, and technical controls shape AI development, deployment, accountability, and social effects.
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