A market trader in Lagos and a banker in London may both be financially responsible. But an automated lending system could still treat one of them as “riskier” before either person ever speaks to a human being. That is one of the hidden problems with AI-driven lending systems. Many digital loan platforms now use automated models to help decide: who gets approved who gets rejected who receives higher interest rates who qualifies for larger loans These systems are often designed to improve speed, reduce fraud, and predict repayment risk. But fairness becomes complicated when AI systems are trained on incomplete, biased, or poorly contextualized data. And in countries like Nigeria, where millions of people work outside highly formal financial systems, this problem becomes even more important. Not all lenders use the same methods, and not all digital lenders rely heavily on AI. However, alternative data scoring and automated risk analysis have become increasingly common in digital lending.…