One of the most promising uses of AI in banking is in credit risk assessment—specifically, predicting who will default on a loan. The traditional credit scoring system, built on FICO scores and basic financial data, is being challenged by AI models that can analyze thousands of variables with greater accuracy.
AI models don't just look at credit history. They assess a broader range of behavioral data, such as payment patterns, spending habits, social signals (where permitted), and even phone usage patterns in some markets. This expanded view helps lenders gauge a borrower’s likelihood of default with more nuance.
This is particularly valuable in markets where many individuals lack a traditional credit history. In regions like Southeast Asia or parts of Africa, “credit invisibles” have historically been excluded from the financial system. AI-based scoring models offer a way to assess creditworthiness using alternative data—opening access to millions of potential borrowers.
Banks using these models report impressive results. For example, Upstart, an AI lending platform, claims its model approves more borrowers while keeping default rates equal or lower than traditional models.
But there are concerns. AI models are only as good as the data they’re trained on. If historical lending practices were biased, the AI can inherit and amplify those biases—discriminating based on race, gender, or socioeconomic status. Regulators are beginning to scrutinize this space, demanding transparency into how AI decisions are made.
Explainability is a major hurdle. Lenders must be able to justify why a borrower was denied, which is difficult when the decision comes from a "black box" algorithm.
Still, the potential is enormous. With proper safeguards, AI can make lending fairer, more inclusive, and more precise.