Cornell Law Review


Algorithmic credit assessment


Algorithmic credit assessment models wear two faces: one operating to perpetuate centuries of racial stratification and otherization in the United States' capitalist system, and one standing poised to correct such injustice by smoking out both inherent human biases and the remnants of past discrimination that are embedded in the system. Although the United States has undertaken efforts to equalize Black Americans' access to opportunities in education, employment, and other critical benefits, no modern and sustained effort has been made to ensure that the Black community is extended credit on sufficient and equal terms, without the effects of prior inequities continuing to pervade financial institutions' lending practices.

This Note explores just a handful of ways in which financial institutions can take advantage of the capabilities of algorithms, big data, and artificial intelligence in order to close the racial gap in wealth and finance. Although institutions' rote use of these technologies is not enough to ensure that racially discriminate practices are not being reinforced through algorithmic credit assessment and lending models, the mindful design, use, and monitoring of fintech lending models presents an opportunity to begin making strides towards bringing Black Americans more meaningfully within the bounds of our financial system and eradicating racial restraints on capitalism.