Financial institutions rely heavily on quantitative analysis and models in most aspects of financial decision making such as credit sanctioning, limit setting, pricing, provisioning, regulatory and economic capital calculation, portfolio management, valuing financial instruments etc. A model uses a mix of mathematical, statistical or other computational logic and human judgment to describe the behaviour of a certain system. Every model is an artifice in the sense that it cannot entirely mimic the complex reality. Decisions made on models that are incorrect or are not performing satisfactorily or used inappropriately exposes the institution to potential direct and indirect consequences. Those consequences should be addressed by active management of model risk including periodic model validation.
Supervisory guidelines such as Basel II IRB, IMA and AMA guidelines link capital requirement to internal models, thus exposing the institution to significant model risks. Our Model Validation Software and Services helps institutions in performance monitoring of its corporate and retail credit models. This is a critical aspect in achieving compliance with IRB guidelines and other regulatory guidance such as OCC (Board of Governors of the Federal Reserve System) 'Supervisory Guidance on Model Risk Management'.
We assist banks in meeting Basel II IRB validation requirements and OCC Model Risk Management requirements by quantitatively validating the rank ordering performance, calibration quality, benchmarking and stability of internal corporate models, retail models, retail pooling frameworks and economic capital models. We validate the overall model as well as its individual components such as individual factors and modules. We make use of various statistical techniques for the purpose of quantitative model validation. Some of these techniques are directly borrowed from numerous scientific disciplines such as machine learning, information systems, medical science etc.
We assist validation and audit teams to review model design, conceptual soundness, structure, internal use, quality of model documentation, integrity of rating process, management oversight etc.
To assess model risk, institutions should maintain a comprehensive set of information for models used internally, or are under development for implementation, or recently retired. We assist in building and managing 'Model Inventory' i.e. a repository of material information for models used internally.
We assist institutions in assessing aggregate model risk based on model materiality, model complexity and model performance.
Use our model validation experience to develop or refine 'Model Risk Policy' including Model Validation Framework, Model Governance Framework and Internal Controls.
We assist 2nd and 3rd Line of Defence against model risk i.e. Model Validation team and Internal Audit Team in validation of internal models to ensure regulatory requirements of independent review of models are met.