2.2.1
| The management of rating models should follow all the steps of the model life-cycle articulated in the MMS. The concept of model ownership and independent validation is particularly relevant to rating models due to their direct business implications.
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2.2.2
| It is highly recommended that institutions develop rating models internally based on their own data. However, in certain circumstances such as for low default portfolios, institutions may rely on the support from third party providers. This support can take several forms that are presented below through simplified categorisation. In all cases, the management and calibration of models should remain the responsibility of institutions. Consequently, institutions should define, articulate and justify their preferred type of modelling strategy surrounding rating models. This strategy will have material implications on the quality, accuracy and reliability of the outputs.
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2.2.3
| The choice of strategy has a material impact on the methodology employed. Under all circumstances, institutions remain accountable for the modelling choices embedded in their rating models and their respective calibrations.
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2.2.4
| Various combinations of third party contributions exist. These can be articulated around the supplier’s contribution to the model development, the IT system solution and/or the data for the purpose of calibration. Simplified categories are presented hereby, for the purpose of establishing minimum expected practices:
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| | (i)
| Type 1 – Support for modelling: A third party consultant is employed to build a rating model based on the institution’s own data. The IT infrastructure is fully developed internally. In this case, institutions should work in conjunction with consultants to ensure that sufficient modelling knowledge is retained internally. Institutions should ensure that the modelling process and the documentation are compliant with the principles articulated in the MMS. |
| | (ii)
| Type 2 – Support for modelling and infrastructure: A third party consultant provides a model embedded in a software that is calibrated based on the institution’s data. In this case, the institution has less control over the design of the rating model. The constraints of such approach are as follows:
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| | | a.
| Institutions should ensure that they understand the modelling approach being provided to them. |
| | | b.
| Institutions should fully assess the risks related to using a system solution provided by external parties. At a minimum, this assessment should be made in terms of performance, system security and stability. |
| | | c.
| Institutions should ensure that a comprehensive set of data is archived in order to perform validations once the model is implemented. This data should cover both the financial and non-financial characteristics of obligors and the performance data generated by the model. The data should be stored at a granular level, i.e. at a factor level, in order to fully assess the performance of the model.
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| | (iii)
| Type 3 – Support of modelling, infrastructure and data: In addition to Type 2 support, a third party consultant provides data and/or a ready-made calibration. This is the weakest form of control by institutions. For such models, the institution should demonstrate that additional control and validation are implemented in order to reduce Model Risk. Immediately after the model implementation, the institution should start collecting internal data (where possible) to support the validation process. Such validation could result in a material shift in obligors’ rating and lead to financial implications. |
| | (iv)
| Type 4 – Various supports: In the case of various supports, the minimum expected practices are as follows:
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| | | a.
| If a third party provides modelling services, institutions should ensure that sufficient knowledge is retained internally. |
| | | b.
| If a third party provides software solutions, institutions should ensure that they have sufficient controls over parameters and that they archive data appropriately. |
| | | c.
| If a third party provides data for calibration, institutions should take the necessary steps to collect internal data in accordance with the data management framework articulated in the MMS.
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2.2.5
| In conjunction with the choice of modelling strategy, institutions should also articulate their modelling method of rating models. A range of possible approaches can be envisaged between two distinct categories: (i) data-driven statistical models that can rely on both quantitative and qualitative (subjective) factors, or (ii) expert-based models that rely only on views from experienced individuals without the use of statistical data. Between these two categories, a range of options exist. Institutions should consciously articulate the rationale for their modelling approach.
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2.2.6
| Institutions should aim to avoid purely expert based models, i.e. models with no data inputs. Purely expert-based models should be regarded as the weakest form of models and therefore should be seen as the least preferable option. If the portfolio rated by such a model represents more than 10% of the institution’s loan book (other than facilities granted to governments and financial institutions), then the institution should demonstrate that additional control and validation are implemented in order to reduce Model Risk. It should also ensure that Senior Management and the Board are aware of the uncertainty arising from such model. Immediately after the model implementation, the institution should start collecting internal data to support the validation process.
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