Machine Learning Model Validation Framework
Finalyse offers a classic approach holding true for Machine Learning model validation. Our model validation scope can be tailored to accommodate your requirements: A purely technical and qualitative exercise focusing on whether a certain methodological choice is fit for the intended purpose. A larger scope covering the implementation of a certain methodology. And ultimately, a validation of the whole process through which the model is deployed within the organisation.
How does Finalyse address your challenges?
Bring in the experience of a multidisciplinary team of experts with extensive exposure to regulatory and non-regulatory models.
Unleash the full power of your ML models: ascertain that you can justify their performance, interpret and explain their outputs and find the right balance in case of multi-objective trade-offs.
Ensure optimal communication between your business lines, subject matter experts and the data scientists creating your ML models.
Ease the compliance with the regulatory expectations of having a proper knowledge of - and widespread awareness around the models in use (TRIM, BCBS 2005, CRD Section II, CRR2 Art. 189 and 293).
Bring in our framework and governance set-up capabilities to ensure the best traction for your models.
Ensure independence of your validation from all possible agency pressure.
Key Features
- A healthy mix of quantitative, business and regulatory profiles for all your ML validation needs.
- We can bring clarity and explainability when complexity blurs the underlying messages, or go looking for that elusive optimisation in your place – just as long as we don’t validate it as well.
- Keeping tracks of the European regulatory progression on ML models as well as more local initiatives to ensure we have the best practices in place.
What Finalyse experts say
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