Model Validation, Tuning, and Testing
Comprehensive model validation services to ensure the accuracy, reliability, and effectiveness of your models
Our solutions are designed to identify potential model risks and ensure that models are being used in a compliant and effective manner. We help you meet regulatory requirements and manage model risk, while improving the performance and efficiency of your models.
- Conduct model performance reviews against historical data and other benchmarks
- Identify and analyze model assumptions, data inputs, and output results
- Determine potential risks associated with the model’s use, such as operational risk, fair lending risk, model risk, and reputational risk
- Use Basel III, Solvency II, and IFRS 9 model review standards
- Provide effective and accurate reporting of the validation process and assessment of the model’s suitability for its intended purpose
- Develop validation frameworks and procedures that can be used to evaluate and validate models across an organization
- Employ relevant data sources, data exploration, and data preprocessing to ensure data quality and relevance
- Apply appropriate model techniques based on the problem statement, data characteristics, and business requirements
- Develop models with considerations to fair lending compliance risks, including disparate impact and disparate treatment associated with proxy variables of prohibited bases
- Perform thorough testing and validation of the model during development against historical data and other relevant benchmarks
- Develop a deployment plan for the model, including considerations for scalability, maintenance, and monitoring, during the development phase
- Analyze model performance metrics and identify areas of improvement
- Test different parameter configurations and hyperparameters to optimize model performance
- Improve generalization and prevention of overfitting by applying regularization techniques such as L1/L2 regularization or dropout
- Evaluate the impacts of data preprocessing techniques, such as scaling, normalization, and feature selection, on the model’s performance
- Apply ensemble methods, such as bagging or boosting, to improve the model’s performance and reduce variance
- Recommend further model tuning or improvement based on active feedback from validation results and feedback from model stakeholders
Ready to Talk?
We work with you to understand your needs, so we can tailor our approach to your engagement. Learn more when you connect with our team.
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