As artificial intelligence (AI) and machine learning (ML) become integral to compliance and regulatory functions, many organisations now recognise the need to apply model risk management (MRM) frameworks retroactively. According to Kevin Lee of Cygnus Compliance Consulting, the “risk footprint of AI models in regulatory compliance are largely risk-neutral or risk-negative. As an industry, we are reducing the risk by taking an AI-assisted approach while leaving the current accountability model untouched.”
The article emphasises that rather than developing entirely new frameworks, firms should enhance existing MRM processes. This involves ensuring the integrity of data ingestion, monitoring for model drift, and testing outputs for bias and accuracy. Lee notes: “The measurement of data movement of bank data into the AI’s pre-processing layers remains a priority… The adage garbage in, garbage out remains true.”
Importantly, the discussion highlights that AI-enabled models do not absolve firms of liability; existing accountability structures remain. By integrating AI governance within established MRM — especially in areas like KYC, fraud detection and regulatory compliance — organisations can bolster resilience without reinventing their frameworks. For insurers and organisations in financial services, the key takeaway is clear: be systematic, ensure transparency, document assumptions, and adapt model validation processes to reflect AI/ML complexities.
For more details and structured learning, please explore our Fraud Risk Management Course.
