Posted: August 14th, 2022

Algorithmic Credit Scoring

Algorithmic Credit Scoring: Examine algorithmic credit scoring’s effects on financial inclusion and economic disparities, with a focus on bias and discrimination.
Algorithmic Credit Scoring: Examine algorithmic credit scoring’s effects on financial inclusion and economic disparities, with a focus on bias and discrimination.
Algorithmic credit scoring is the use of machine learning (ML) and econometric methods to analyze credit applications and predict the likelihood of loan default. It is widely used by banks and financial institutions to automate and streamline the decision-making process for providing credit. However, algorithmic credit scoring also poses significant challenges for financial inclusion and economic justice, as it may perpetuate or exacerbate existing bias and discrimination against groups defined by race, sex, sexual orientation, and other attributes.

One of the main sources of algorithmic discrimination in the credit domain is the quality and quantity of data used to train the ML models. According to a large-scale study by Blattner and Nelson (2021), minority and low-income groups have less data in their credit histories, which makes their credit scores less accurate and more noisy. This means that the predictions based on these scores are less reliable and more prone to error. As a result, these groups face higher rejection rates, higher interest rates, and lower loan amounts than majority and wealthier groups, even when they have similar creditworthiness (Blattner and Nelson 2021).

Another source of algorithmic discrimination is the potential bias in the data itself, which reflects historical and structural inequalities in the credit market. For example, Garcia et al. (2023) conducted a systematic literature review of 78 papers on algorithmic discrimination in the credit domain and found that most of the research focused on direct discrimination due to bias in the dataset. They also found that most of the attention was devoted to the mortgage market, where minority groups face differential treatment in terms of fees, parcels, and interest rates. Moreover, they noted that most of the papers adopted a computer science, law, or economics perspective, while neglecting other social science disciplines that could provide a deeper understanding of the causes and consequences of algorithmic discrimination (Garcia et al. 2023).

Therefore, algorithmic credit scoring is not a neutral or objective tool, but rather a complex socio-technical system that embeds and reproduces existing power relations and social norms. To address this issue, it is not enough to make the algorithms more fair or transparent, but rather to challenge the underlying assumptions and values that inform their design and implementation. This requires a multidisciplinary and participatory approach that involves not only researchers and practitioners, but also regulators, policymakers, civil society organizations, and affected communities. Only by engaging with diverse perspectives and experiences can we ensure that algorithmic credit scoring serves the public interest and promotes financial inclusion and economic justice.

References:

Blattner L., Nelson S., 2021. “Bias isn’t the only problem with credit scores—and no, AI can’t help.” MIT Technology Review. https://www.technologyreview.com/2021/06/17/1026519/racial-bias-noisy-data-credit-scores-mortgage-loans-fairness-machine-learning/

Garcia A.C.B., Garcia M.G.P., Rigobon R., 2023. “Algorithmic discrimination in the credit domain: what do we know about it?” AI & Society. https://link.springer.com/article/10.1007/s00146-023-01676-3

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