A Model for Strategic Investment in African Infrastructure: Using AI for Due Diligence and Portfolio Optimization

Infrastructure investment in Africa presents a significant opportunity to bridge development gaps, stimulate economic growth, and achieve sustainable development goals. However, investors often face substantial risks due to opaque regulatory environments, data scarcity, and socio-political instability. This paper proposes an innovative artificial intelligence (AI)-driven model to enhance strategic investment decisions in African infrastructure projects. The model integrates AI-based due diligence mechanisms with portfolio optimization algorithms, addressing both pre-investment analysis and post-investment asset management. The AI-driven due diligence module employs natural language processing (NLP), machine learning (ML), and geospatial analytics to aggregate and interpret multi-source data including policy documents, environmental impact reports, and satellite imagery. This enables real-time risk assessment and project viability analysis. Simultaneously, the portfolio optimization component uses predictive analytics and reinforcement learning to maximize returns while minimizing exposure to geopolitical, financial, and operational risks. By simulating various economic scenarios and investment strategies, the model assists investors in aligning infrastructure portfolios with regional development priorities, climate resilience targets, and long-term profitability. The model was tested across case studies involving transportation, energy, and digital infrastructure projects in East and West Africa. Results demonstrated improved transparency, shorter decision-making cycles, and optimized capital allocation. The system also proved valuable for institutional investors, development finance institutions (DFIs), and public-private partnerships (PPPs) seeking to scale operations in emerging markets. Additionally, the model enhances ESG (Environmental, Social, and Governance) compliance by incorporating real-time stakeholder sentiment analysis and sustainability metrics into investment decision-making. This research contributes to the growing literature on AI applications in development finance and underscores the potential of intelligent systems to catalyze infrastructure development in underfunded regions. By leveraging AI for due diligence and portfolio management, investors can navigate Africa’s complex investment landscape more efficiently and responsibly. The paper concludes with policy recommendations and a roadmap for integrating the model into regional investment frameworks.