Keywords
Artificial Intelligence, Consumer Financial Data, Horizontal Mergers, Machine Learning, Non-Horizontal Mergers
Abstract
This Article explores the potential competitive implications of non-horizontal mergers where they involve extensive consumer data, including consumer financial data. As data become increasingly central to firm strategy, mergers between data-rich firms, while potentially leading to positive outcomes, can also create market power in ways not entirely accounted for by traditional antitrust theory. The Article considers some of these implications. It introduces new metrics for valuing data sets held by merging firms that could help competition authorities evaluate market impacts more effectively. The Article then suggests potential tools to mitigate anti-competitive effects of data-rich mergers. It advocates for further research to adapt competition policy to data-centric mergers, all with a view to maintaining open, innovative and competitive markets in the digital and data economy.
Recommended Citation
Consumer Financial Data: 30 Fordham J. Corp. & Fin. L. 301 (2025).
Included in
Banking and Finance Law Commons, Business Intelligence Commons, Consumer Protection Law Commons, Finance and Financial Management Commons