Celent a Boston-based financial research and consulting firm, outlines a framework geared toward mitigating inherent risks borne by financial institutions’ complex network of models in a new report, “Model Validation Practices: Achieving Value-Added.”
Key findings in the report include:
* The upsurge in usage of models, sophistication of underlying theories, and complexity of the IT environment have firmly placed model risk (the risk that models do not perform as intended) on regulator and senior executive agendas. Building a model governance framework to contain this risk has become a prevalent risk management practice. Model validation, essentially a peer review system with the ultimate goal of providing efficient quality control, has been an integral part of this effort.
* Accounting troubles and tight regulatory controls around risk measurement models led the first model validation wave, primarily as a one-off exercise and/or a pure compliance activity. The next wave will be characterized by repositioning beyond compliance towards a streamlined, value-adding model validation process. Completely eliminating model risk through controls is impossible; therefore institutions need to strike the right balance between costs and benefits.
* Financial institutions have been pressured by the market and by regulators to improve information management. Initial efforts focused on enhancing raw capabilities followed by employment of those capabilities to generate competitive advantage in the marketplace. Accordingly, most institutions installed highly complex models. However, they generally have been incorporated without proper governance frameworks to contain the resulting model risks. Model governance, and in particular model validation, needs to be an integral part of the overall model life cycle to give the requisite confidence to senior executives.
* The past few years have seen the establishment of model validation units. However, best practices documented in this report are only employed by a minority, and it would be optimistic to conclude that the state of the market is close to the end game. Converging to an efficient steady state will require a focus on positioning of the model validation function, design of the actual validation process, and resource constraints.