Machine learning for financial inclusion in agriculture: A study of AI-based credit scoring tools in rural Nigeria

This study looks at the impact of artificial intelligence (AI) and machine learning (ML) in increasing financial inclusion through better credit scoring systems, with a focus on rural Nigeria's agricultural industry. Traditional credit models frequently exclude low-income individuals and smallholder farmers since they are based on official credit histories and organised income data. AI/ML-driven models, which use alternative data such as mobile transactions and utility bills, provide a more comprehensive approach to creditworthiness assessment. The review examines key algorithms such as logistic regression and random forests, as well as their benefits and ethical concerns, such as data privacy, algorithmic bias, and transparency. It also showcases real-world applications, such as Carbon in Nigeria and mobile-based loans in Kenya, which demonstrate better access to credit. However, significant challenges remain, such as digital illiteracy, inadequate infrastructure, and insufficient regulatory frameworks. According to the paper, while AI has great promise, its success is dependent on supportive policy, ethical oversight, and investments in digital infrastructure. Future study should look at the true impact of digital credit instruments on rural lives, as well as how these technologies might be tailored to promote social fairness and sustainable development.