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.
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.
Machine learning is today’s buzzword and it has gone through some phenomenal changes over the last few years. However, despite widespread adoption, machine learning models remain mostly black boxes. Hence it is essential to have techniques for explaining these black boxes in an interpretable manner. This article looks at the methods that are most used for interpreting machine learning models: SHAP, LIME and CP Profiles. It discusses advantages and drawbacks of each and shows how are they being used in practice.
ReadThis article discusses the benefits of applying advanced analytics in the development of behavioural scoring models. It investigates how Machine Learning techniques can be used to model the behavioural scores of consumers in each step of the development and model assessment. Some concerns regarding the usage of Machine Learning in behavioural scoring models are addressed.
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