This paper explores regulatory arbitrage from a legal point of view. I start from the assumption that legislators will sometimes wish to prevent regulatory arbitrage and examine legal tools available to this end. To back up the underlying assumption, I present two perspectives on the phenomenon of regulatory arbitrage. One perspective stresses its competitive element, the other one focuses on instances of arbitrage as unwanted avoidance of a legal regime. It is suggested that from both perspectives we will find that – at least sometimes – regulatory arbitrage is unwanted. I move on to illustrate how EU and U.S. legislators have dealt with an example of unwanted arbitrage. The main part of the paper then deals with legal tools to suppress arbitrage. The main focus is on legislative drafting techniques such as choosing a narrow wording, a broad wording, anti-evasion rules or the concept of abuse. I conclude with a glance at problems of regulatory arbitrage in a corporate setting.

ARGUMENT by analogy is one of the oldest methods of decision making. Whenever the similarity between two situations induces someone to decide one case like another, an analogy is drawn. Argument by analogy also forms an integral part of legal reasoning. Arguably, every legal tradition employs some version of it to justify judicial decisions. European law has only just started to develop its own distinct jurisprudence. As the various judicial systems present in the European Union struggle for recognition of their legal heritage, the way in which arguments by analogy will be used on an European level is likely to combine different approaches.

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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

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