Are there limits to machine learning in trade surveillance?

Nick Wallis says that blending rules-based, machine learning and automation techniques can help overcome trade surveillance challenges.

Compliance professionals face the daunting task of making sense of mounds of data, alerts, and shifting regulations. The power of AI and machine learning (ML), as evidenced by the text-generating software ChatGPT, has ignited imaginations as to how new tools can empower teams to achieve more accurate results more quickly. For compliance officers, the allure of ML is that it can address the problems inherent in most surveillance systems: They generate too many alerts and false positives, leading

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Systematic tools gain favor in fixed income

Automation is enabling systematic strategies in fixed income that were previously reserved for equities trading. The tech gap between the two may be closing, but differences remain.

Why recent failures are a catalyst for DLT’s success

Deutsche Bank’s Mathew Kathayanat and Jie Yi Lee argue that DLT's high-profile failures don't mean the technology is dead. Now that the hype has died down, the path is cleared for more measured decisions about DLT’s applications.

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