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“Essentially all models are wrong, but some are useful,” British statistician George E. P. Box declared. Despite progress made in machine learning and artificial intelligence, digital credit models risk being considered “wrong, but useful” – useful for certain groups such as urban salaried workers, but wrong as they leave behind large segments of the economy whose data trails are too faint or complex for a credit scoring algorithm.

CFI recently explored how automated decision-making tools for digital credit are playing out for one demographic in India – the more than 54 million interstate migrants – and what this might teach us about the ways data inputs and algorithms intersect with the digital lives of those at the margins.

Photo courtesy of Brooke Patterson/USAID.