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Compliance

As explained in Financial AI in Practice

Regulatory compliance is adherence to applicable laws, regulations, supervisory requirements, and prescribed financial controls.

Compliance means adhering to applicable requirements and controls. In financial AI, it shapes both risk management and the checks placed around generated responses. section 1.3.1 section 3.2.1

Two operational roles stand out:

  • Fraud and anti-money-laundering (AML) systems can strengthen compliance by detecting suspicious activity while reducing false alarms. section 1.3.1
  • Generative AI systems can ground responses in internal compliance policies, then apply output filters for accuracy and legal disclaimers. High-risk responses, such as financial advice, may require qualified human approval. section 3.2.1

AML screening

Danske Bank reported reducing false positives in AML screening by 60% while increasing detection of illicit activity. section 1.3.1

How Financial AI in Practice covers it

How deep it goes
Describes AML benefits and output safeguards, but does not detail specific laws or control procedures.
Examples
Cites Danske Bank’s AML screening results and compliance filters for generated financial advice.
What you can do after
Understand how compliance informs fraud controls and safeguards for generated financial responses.
How it connects
Links compliance with fraud detection, anti-money-laundering, retrieval-augmented generation, and human-in-the-loop review.

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This explainer was generated by Manning's knowledge graph from the text of Financial AI in Practice. The section links point to the parts of the book it draws on.