Ghana’s financial landscape has experienced rapid digital transformation over the past decade, yet underserved households articularly in low-income, rural, and informally employed segments continue to face significant barriers to accessing formal credit and appropriate financial products. Traditional credit-scoring methods depend heavily on collateral, stable income histories, and formal identification, which exclude a large portion of Ghanaian households participating in informal economic activity. As digital banking channels expand through mobile money platforms, fintech integrations, and agent networks, artificial intelligence (AI) presents a powerful opportunity to close long-standing inclusion gaps by analyzing non-traditional behavioral and transactional data. This paper proposes an AI-driven credit and product-suitability framework optimized for underserved Ghanaian households operating within digital ecosystems. The model incorporates alternative data such as mobile money transaction patterns, airtime usage, merchant payments, remittance flows, savings cycles, and household-level consumption signals. Machine learning methods including ensemble classification, gradient boosting, and representation learning are applied to predict repayment capacity, detect financial distress early, and match customers with suitable microcredit, insurance, and savings products. The framework also integrates fairness metrics to minimize algorithmic bias across gender, geography, and income categories. Results from simulated deployments demonstrate that AI-driven suitability models significantly increase loan approval for previously excluded customers while maintaining portfolio quality. By leveraging behavioral patterns rather than collateral requirements, the system enhances credit access for rural farmers, market traders, micro-entrepreneurs, and gig-economy workers. The paper concludes that embedding AI into Ghana’s digital banking infrastructure can accelerate financial inclusion, strengthen household resilience, and support evidence-based product innovation tailored to the needs of underserved populations.