There is multiple elements to your question. Two ways where these systems may give out more loans than is strictly profitable, and they are both investments:

There is multiple elements to your question. Two ways where these systems may give out more loans than is strictly profitable, and they are both investments: - Fairness. If you have a variable race and a zip code, you could account for discrimination via redundant encodings, while still using the feature for the optimal trade-off between a fairness criteria and model performance. - Exploration. Concept drift (the correlational and causal meaning of variables shifts over time) can introduce wrong predictions. If all you have is few samples from a zip code, the model will always be uncertain. You can counter this by exploration and active learning: gather samples, not because this makes the model max-profit, but gather samples, to better learn how to predict these samples in the future. But yes, giving out too many loans to minorities, may very well lead to further crisis and defaults, and tainting the credit scores of people will low access to finances even further. A bit like how well-meaning people donate money and food to Africa during Christmas time, then a few months later when donations subside, there are increases in famines. There is such a thing as "being too good".