The MMS and the MMG will be effective one day after their publication date.
2.2.2
All institutions are expected to identify gaps between their practice and the MMS and MMG and, if necessary, establish a remediation plan to reach compliance. The outcome of this self-assessment and the plan to meet the requirements of the MMS and the MMG must be submitted to the CBUAE no later than six (6) months from the effective date of the MMS.
2.2.3
Institutions must work towards compliance in a proactive manner. They must demonstrate continuous improvements towards meeting these requirements within a reasonable timeframe. This timeframe will be approved by the CBUAE following a review of the self-assessments. The CBUAE will take a proportionate view in its assessment of the proposed time to reach compliance, taking into consideration the size and complexity of each institution. The remediation plan and the associated timing must be detailed, transparent, and justified. The plan must address each gap at a suitable level of granularity.
2.2.4
Institutions, which repeatedly fall short of the requirements and/or do not demonstrate continuous improvement, will face greater scrutiny and could be subject to formal enforcement action by the CBUAE. In particular, continuously structurally deficient models must be replaced and must not be used for decision-making and reporting.
2.2.5
The path to remediation may involve reducing the number and/or complexity of models in order to improve the quality of the remaining models. Subsequently, and subject to remediation needs, the institution could increase the number of models and/or their complexity while maintaining their quality.
2.2.6
Institutions must achieve and maintain full compliance with respect to the general principles described in Part I and Part II of the MMS. For the MMG, whilst alternative approaches can be considered, the focus is on the rationale and the thought process behind modelling choices. Institutions must avoid material inconsistencies, cherry-picking, reverse-engineering and positive bias, i.e. modelling approaches that deliberately favour a desired outcome. Evidence of an institution defying the general principles in this way will warrant a supervisory response ranging from in-depth scrutiny to formal enforcement action.
2.2.7
For statistical models in particular, institutions must focus on the suitability of their calibration, whether these models are relying on internal data or external data. Lack of data will not be an acceptable reason for material models to fall short of these requirements. Instead, institutions must implement temporary solutions to mitigate Model Risk until models based on more robust data sets are implemented. Institutions must avoid excessive and unreasonable generalisations to compensate for lack of data.