Our dedicated experts, regulatory knowledge, and proprietary tools and frameworks enable clients to manage their model validation programs and targeted model reviews effectively and efficiently.
Risk Dynamics helps clients validate models of all types, in financial services and beyond. To ensure our clients’ ongoing success, we continually update our proprietary, global validation framework to reflect the latest modelling techniques, regulatory changes, and business-specific needs. Our comprehensive approach can be applied to a wide range of engagements, from large-scale, programmatic validations to targeted reviews of individual models or families.
In our 15 years of model validation experience, we have built our clients’ validation capabilities by developing frameworks, providing tools and processes to boost efficiency, and imparting our expertise. We have helped global, regional, and national financial institutions fulfill both internal and regulatory requirements for independent model validation and review. We have also provided clients with added capacity when needed.
A leading global financial institution engaged Risk Dynamics on an ongoing basis to validate a broad set of models in multiple locations. The validations - which cover all types of models across the client inventory - are carried out seamlessly by our global team at client sites in Asia, Europe, and North America. The team is conducting both conceptual and technical reviews.
The client has benefited from our ongoing updates of the validation framework to incorporate the latest technical advances and regulatory requirements in each jurisdiction. Our team has also built up the skills of internal validation teams and provided added capacity during periods of peak demand. Further, the client has gained a workbench of validation tools and processes that was deployed for increased efficiency in model reviews and in the interactions with modelers.
To detect early signs of possible compliance breaches, a large European bank has deployed a complex machine learning model that uses natural language processing to monitor internal communications, such as emails and chat messages.
The bank engaged Risk Dynamics to conduct a bespoke validation exercise to confirm that the model was sound and effective. The validation uncovered several potential risks related to technical issues, data quality, ongoing monitoring, and model documentation. To address these risks, we developed a challenger model, which improved performance and reduced the number of sub-models needed. We also developed clear testing guidelines and an ongoing monitoring plan to ensure more consistent model performance over time.