The Norms of Algorithmic Credit Scoring
Bibliographic Data
| ID | 21300034 |
|---|---|
| Authors | Nikita Aggarwal (0000-0002-7686-4257, corresponding author) |
| Year | 2021 |
| Volume | 80 |
| Issue | 1 |
| Pages | 42-73 |
| Publication date | 2021-03-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | The Cambridge Law Journal (JOURNAL) |
| Journal identifiers | ISSN: 0008-1973 • E-ISSN: 1469-2139 |
| Publisher | Cambridge University Press (CUP) (PUBLISHER) |
| DOI | 10.1017/s0008197321000015 |
| OpenAlex | W3150272442 |
| Language | EN |
| Citations received | 5 |
This article examines the growth of algorithmic credit scoring and its implications for the regulation of consumer credit markets in the UK. It constructs a frame of analysis for the regulation of algorithmic credit scoring, bound by the core norms underpinning UK consumer credit and data protection regulation: allocative efficiency, distributional fairness and consumer privacy (as autonomy). Examining the normative trade-offs that arise within this frame, the article argues that existing data protection and consumer credit frameworks do not achieve an appropriate normative balance in the regulation of algorithmic credit scoring. In particular, the growing reliance on consumers’ personal data by lenders due to algorithmic credit scoring, coupled with the ineffectiveness of existing data protection remedies has created a data protection gap in consumer credit markets that presents a significant threat to consumer privacy and autonomy. The article makes recommendations for filling this gap through institutional and substantive regulatory reforms
Actuarial science · Allocative efficiency · Autonomy · Business · Computer security · Consumer protection · Credit enhancement · Credit history · Credit reference · Credit risk · Data Protection Act 1998 · Economics · Frame (networking) · General Data Protection Regulation · Law and economics · Microeconomics · Normative · Political science · Public economics · Underpinning · Computer Science · Finance · Law · Privacy-Preserving Technologies in Data
Toward an evolving framework for responsible AI for credit scoring in the banking industry
Sovereign Credit Ratings, Transparency, and Algorithms
Trust, Explainability and AI
Data, data, data” – post-Brexit data governance and its relation to the UK’s economic model
Algorithmic Credit Scoring in Vietnam
| Unique citing works | 5 |
|---|---|
| Citations per year | 1.67 |
| Citation span | 2023 - 2026 (4) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 5 |