Abstract
Financial market infrastructures (FMIs) generate immense amounts of data that is valuable for many purposes ranging from optimising liquidity to enabling better risk management and generating insights about the economy. This paper discusses practical outcomes that can be achieved with advanced analytics using artificial intelligence and machine learning algorithms, how to get there and some potential pitfalls along the way. The paper should provide sufficient insight for readers to consider new technologies and approaches to meet industry guidelines. Additionally, it shares best practices on how, as a result of increasing interconnectedness in the global financial markets, FMIs and their regulators need to help one another improve their anti-money laundering posture and systemic risk exposure.
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