
BNP Paribas has returned to profit in the third quarter after a record fine for violating US sanction controls in Sudan, Iran and Cuba.
The $8.9bn fine from US regulators hurt the bank’s first half results, and it reported a record €4.32bn loss in the second quarter. BNP also faced a criminal charge as well as the fine for violating US sanction controls in Sudan, Iran and Cuba between 2002 and 2012.
The bank was also given a one-year ban on clearing some dollar transactions to start in January.
This was only the second quarterly loss in the history of the Paris-based lender, formed in 2000 in a merger of the formerly state-owned retail bank Banque Nationale de Paris and the investment bank Paribas. For the first half overall, pre-tax losses were €1bn.
Chief executive Jean-Laurent Bonnafé, who faces a challenge to restore the bank’s reputation, told the Financial Times:
“BNP Paribas Group delivered this quarter a very good overall performance thanks to its diversified business and geographic mix. The good sales and marketing drive confirms the loyalty of our institutional, corporate and individual clients.”
Whitepapers
Related reading
Central banks best suited to issue digital currencies
By Aaran Fronda A recent report by the Official Monetary and Financial Institutions Forum (OMFIF) said that central banks rather than private ... read more
Instant payments: innovations inbound for corporates
In 2020, instant payments look set to continue their current trajectory to become the biggest trend in payments. While these schemes already offer numerous benefits to corporates, leveraging innovations such as APIs and request to pay will go some way to unlocking their full potential, argues Michael Knetsch
Obstacles exist for banks to meet ECB’s instant payments goal
The cost of joining instant payment platforms will be one of many hurdles banks and payment services providers must overcome to meet ... read more
Banks must be aware of “biases” in data used to train ML models
Financial institutions need to be conscious of biases in the historical data that is being used to train machine learning (ML) models, ... read more