Model Risk Management (MRM) is an important aspect of any bank’s activity, but it is not an area in which regulation and best practice have tended to change frequently. Until recently, not even the rapid spread of artificial intelligence / machine learning (AI/ML) modeling techniques across all areas of banking has provoked clear change in regulations or best-practice frameworks.

This summer’s publication of the UK’s SS 1/23, the first regulation specific to AI/ML model risk in banking, is thus a key milestone and an opportunity to learn about and prepare for the global regulatory response to AI/ML.

In this whitepaper, we:

  • Summarize the risks presented by AI/ML techniques.
  • Summarize the current state of regulation, with emphasis on ongoing work and recent regulatory statements.
  • Consider the implications of SS 1/23.
  • Consider the emerging regulatory trends.
  • Outline the requirements that are likely to emerge for MRM and related enterprise functions.



Ben Peterson

Ben Peterson, Treliant’s Data Lead for EMEA, is a technology leader with more than 20 years’ experience in Financial Services and fintech.  He understands the role that strong data management plays in increasing revenue and reducing risk, and believes that data management can have a compelling RoI at both program…