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Machine learning security risks in financial services

Working at DC

Machine learning (ML), under the banner of Artificial Intelligence, is being quickly
integrated into core financial services use cases. These systems are vulnerable to
hacking or malicious interference in the same way as traditional systems, but they
have unique vulnerabilities. 

From data poisoning to adversarial attacks, this paper explores examples of ML-based exploits in financial services, as well as drawing a link between these risks, and existing standards-based and regulatory calls to action to implement organisation and technical controls to mitigate them. Safe and secure AI is a key adoption challenge.