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Steven Lofchie is the Chief Legal Architect and a co-founder of Caffrey.AI, an AI platform built for law firms, banks, and the financial regulatory industry. He is also a partner at Norton Rose Fulbright, where he advises financial institutions on securities laws, the Commodity Exchange Act, and broker-dealer regulation. Ranked by Chambers USA every year since 2007, he is the author of “Lofchie’s Guide to Broker-Dealer Regulation,” long considered the leading treatise on the subject.

Recent Articles & Comments

This enforcement action makes a somewhat questionable use of the term "supervision." One ordinarily thinks of "supervision," in the broker-dealer context, as referring to the direction and control by a "principal" at a broker-dealer of other employees of the broker-dealer. That seems not to have been the issue here.

The associated persons whose conduct motivated the enforcement action did not "supervise" customers, at least in the sense in which the word is ordinarily used. Rather, it…

There could hardly be a greater contrast in regulatory philosophies than there is between Chair Gensler and Commissioner Peirce. While Ms. Peirce is currently on the losing end of 3-2 votes, her remarks and dissents are significant in that they emphasize the possibility of individual autonomy and an acceptance of an individual's willingness to take risk. In the best of all possible worlds, there might be some possibility for reconciliation of such opposing views, but these speeches suggest…

Setting to the side the SEC's alleged failure to explain itself, the relevant legal precedent at issue here is not ; it is (holding that demand notes sold to retail investors are securities). The security at issue in this case is not the USD Coin (which is probably not a security at all); it is the loan of the asset to Coinbase. In short, the SEC would almost certainly assert that Coinbase is offering unsecured interest-bearing "notes" to retail investors; the fact that the retail investors…

The discussion of "biases" is an important read for firms seeking to implement AI-based decision-making. The intellectual difficulty is that a "bias" would seem to suggest a false outcome (e.g., data integrity or performance issues for the AI or ML application); but in this context the definition also includes an outcome that is viewed as not socially desirable.

If the term "bias" includes outcomes that are not socially desirable, a firm may not be able to correct "bias" by improving…