Responsible A.I.-based Credit Scoring – A Legal Framework

Abstract

The paper proposes a legal framework to evaluate emerging FinTech methodology based on alternative data and machine learning to score borrowers. Instead of conventional variables, novel methods rely on information gathered from social networks, “digital footprints”, mobile phones or GPS data. Correlating these with repayment of loans is promoted as triggering precise predictions of probability of default. Borrowers profit if their profile falls outside of classic scoring checks but performs well under the new regime. Borrowers are disadvantaged if the new methods entail disparate impact for groups which are protected under anti-discrimination laws. Additionally, data may be collected without their consent or used in a way they don’t understand. Two contributions to the debate are submitted. Firstly, a comparative assessment of EU and U.S. data protection and anti-discrimination laws suggests what might qualify as responsible A.I.-based scoring. Secondly, public and private enforcement mechanisms are explained Artificial Intelligence, Credit Scoring, Biased A.I., GDPR, Discriminative Lending Practices, A.I. compliance, Scoring and Banking Regulation, ECOA, FCRA, FICO score

AI in credit scoring: A comprehensive review of models and predictive analytics

“…Individuals with unconventional employment patterns, limited credit histories, or those belonging to marginalized communities may benefit from AI models that consider a wider range of factors, promoting a more inclusive credit ecosystem. AI algorithms, when designed and implemented ethically, have the potential to reduce discriminatory practices in credit scoring (Langenbucher, 2020). By focusing on objective and relevant factors rather than demographic information, AI models strive to provide fair assessments, mitigating biases that may have been prevalent in traditional credit scoring practices.…”

Regulating Fintech and BigTech: Reconciling the Objectives of Financial Regulation and Promoting Competition

“…The proposed Regulation for artifi cial intelligence (AI) systems 112 purports to set out governance expectations of systems with unacceptable, high, limited or minimal risks to persons and society, but regulatory delineations as well as governance standards and design are subject to controversy and critique. 113 When introduced, this cross-cutting legislation will affect not only fi ntech businesses applying algorithmic credit scoring 114 or algorithmic compliance such as with anti-money laundering, 115 but also other sectors dealing with self-learning systems in production, marketing and other operations, such as in medical diagnostics. 116 We also observe examples of more limited forms of reconsolidating regulatory initiatives such as in the Digital Operational Resilience Act 117 (DORA) and proposed Regulation for Market Infrastructures using Distributed Ledger Technology (DLT).…”

Transparency as Manipulation? Uncovering the Disciplinary Power of Algorithmic Transparency

“…For example, the Society of Human Resources Management (SHRM) conducted a survey showing that about 60% of employers in America perform credit background checks for their job candidates. 12 Insurance companies now use credit checks to decide which insurance rates should be assigned to drivers, houseowners, and even patients (Langenbucher, 2020). There is even a dating website, which promotes itself as a place "where good credit is sexy," trying to match subscribers with credit scores.…”