Editorial

Embracing SS1/23: How AI and Machine Learning are Shaping MRM ​

Model risk management has become a critical aspect of risk management frameworks within financial institutions due to the increasing reliance on models for decision-making. SR11-7 (USA), TRIM (EU) and more recently SS1/23 (UK) provide comprehensive guidelines to ensure that model risk is effectively identified, measured, monitored, and controlled. ​

Contributor

George has more than 22 years of Financial Services experience, specialising in Trading and Risk Management.

George Petropoulos
Chief Product Officer, Pricing & Risk

Out of the three policies the SR11-7 and SS1/23 are holistic comprehensive frameworks for model risk management, with SS1/23 reflecting updates in regulatory expectations due to advancements in technology and increased complexity in financial models: ​

  • Technological Advancements: SS1/23 addresses AI and machine learning models, emphasizing explainability and new validation techniques. ​
  • Data Governance: SS1/23 places greater emphasis on data management, recognising its critical role in model reliability. ​
  • Cultural Focus: SS1/23 explicitly incorporates cultural and behavioural aspects into MRM practices. ​
  • Regulatory Specificity: SS1/23 provides more detailed and prescriptive guidance compared to the principle-based approach of SR11-7. ​


For a more detailed analysis between SR11-7 and SS1/2, please click here.

​The Prudential Regulation Authority's (PRA) Supervisory Statement 1/23 (SS1/23)1 introduces a critical update to how financial institutions define and manage models. The Policy is comprehensive, clear and live and applies to all regulated United Kingdom (UK)-incorporated banks, building societies and PRA-designated investment firms with internal model approval to calculate regulatory capital requirements.

Key Principles
In scope, firms must demonstrate that they comply with all principles: ​

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How Can Delta Capita Help You Achieve SS1/23 Compliance and Transform Your Model Risk Management?​​​

These areas will require some careful consideration by firms given the technological challenges that surround them. For example: ​

  • Model inventories must allow the incorporation of new model attributes and should be flexible enough to adjust the model lifecycle workflow.  ​
  • As the use of AI and machine learning techniques is increasing firms should be able to explain model outputs confidently and establish smart monitoring to improve confidence of use.  ​
  • Standardise the validation of externally developed models and bring them to internal standards of scrutiny. ​


Delta Capita’s Model Risk Management practice combines deep industry and domain expertise with the use of highly skilled resources and specialist vendor technologies to deliver bespoke results-focused solutions for our clients. Our team has proven modelling and model validation experience across the spectrum of models found in financial services firms.​ ​

Our practice can facilitate a firm’s roadmap to SS1/23 compliance and can support the transformation journey often required to meet SS1/23 and best practices.