Algorithms, Creditworthiness, and Lending Decisions

From a contract law perspective, this chapter addresses the implications of algorithmic creditworthiness assessments in credit agreements. On the assumption that (a) access to credit equals opportunity, (b) the current debate turns on the risks of algorithmic bias and discrimination on creditworthiness assessments, and (c) the much-defended “duty to explain” automated decisions under GDPR, this chapter delves into the multifaceted legal implications of algorithms use in the credit-scoring and in the lending decision-making processes. The chapter starts by challenging common perceptions about algorithmic decisions, focusing on the concept of «opacity» in decision-making processes. It outlines key aspects of opacity and its impact on creditworthiness assessment, highlighting the complexities of creditworthiness and the legal obligations of lenders, borrowers, and credit bureaus. Ultimately, the study concludes that the epistemic challenge posed by algorithm-based reasoning is less problematic than that of human decision-making. As a consequence, it can be argued that, under the purview of contract law, there is no valid justification to treat an algorithmic lending decision to deny or cut credit differently or more severely within the specific lender-borrower relationship, provided the same degree of opacity is present. Close attention is paid to algorithmic decisions considered discriminatory or unfair, as these decisions lie behind the current concerns about algorithmic accountability.