Claims fraud reduction in 9 months. Unified intelligence across all 36 states.
Proof Of Delivery
We do not describe what AI could do for African institutions. We show what it has already done - with named clients, documented outcomes, and reproducible models.
Claims fraud reduction in 9 months. Unified intelligence across all 36 states.
Faster claims processing after the unified Health Data Lake deployment.
Credit model for IoT-connected smallholder farmers. No formal credit history required.
Underwriting intelligence on Pan-African risk data. Pricing accuracy transformed.
Public Health · Nigeria
Business Problem
Fragmented records across 36 states, high claims fraud, and zero real-time policy visibility – creating significant fiscal leakage and coverage gaps for millions of beneficiaries. No unified intelligence layer across the national health system.
Scope of Work
Client Voice
“AICE Africa didn't just deploy a solution – they built the internal capability to sustain and evolve it. That's the difference between a vendor and an architect.”
Director of Technology · NHIS Nigeria · 2024
Agritech · East Africa
Live
East Africa farmer portfolio
CNN/LSTM credit risk model for IoT-connected smallholder farmers with no formal credit history. Mobile, IoT, and climate signal integration. Scalable replication across East African markets.
Reinsurance · Pan-Africa
Real-time
Underwriting intelligence deployed
Predictive underwriting and actuarial intelligence on Pan-African risk data. Real-time pricing accuracy and risk exposure management across the Pan-African reinsurance portfolio.
Africa needs architects.
We are those architects. Begin with an Intelligence Audit — a 2–4 week assessment that delivers your AI Maturity Score, gap analysis, and a personalised roadmap.
Does your organisation have a documented data strategy that covers collection, governance, and quality standards?
Do you have dedicated AI or data science talent in-house, or a clear plan to build it?
Has your organisation defined an AI ethics policy or governance framework?
Is your data infrastructure (cloud, APIs, pipelines) ready to support AI workloads?
Do you have a clear AI strategy aligned to your institutional objectives for the next 3 years?
Does your organisation prioritise owning its AI models and data, rather than relying entirely on third-party platforms?
Are there executive-level champions actively driving AI adoption within your organisation?
Have you mapped specific use cases where AI could transform your operations or services?