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.
You are using an ML model to screen credit applications. You are not aiming at minimizing the level of defaults, but are in fact trying to optimise your risk-return payoff. And you want to do it in a way compliant with the European Regulatory Framework, implying e.g.:
Working with Finalyse on your Model Validation Framework will allow you to leverage on the broad experience we have accumulated in the area of risk measurement and management, as well as on varied expert profiles brought together under a team.
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.Read
This Expert input works as an overview of the basis of most available machine learning techniques and serves as a great stepping stone to get into the intricate world of ML. Whilst mostly written with credit risk in mind – offering some advice for the use of machine learning to help us model Expected credit loss and its components (PD, LGD, EAD), the list of the potential techniques depicted steps far beyond this relatively singular use and examines a multitude of approaches, ranging from Supervised ML (Decision trees, Artificial Neural networks, etc.) through ensemble ML (Random forests, Gradient boosting), to unsupervised ML (Deep learning, Clustering methods, etc.). The input concludes with several general tips and tricks regarding machine learning.Read