@misc{9242278,
  abstract     = {{For financial institutions, estimating the probability of default is central to credit-risk modelling. This is traditionally done using logistic regression, but machine learning methods have shown promising results because of their ability to capture more flexible relationships. However, the complexity of such models makes them difficult to interpret and consequently difficult to justify to regulators.

This thesis explores the use of functional ANOVA decomposition to construct an interpretable representation of depth-limited gradient-boosted decision trees for credit-risk modelling. The aim is to evaluate whether the resulting decomposition can be regarded as an inherently interpretable model that aligns with regulatory expectations.

The results show that the approach successfully reconstructs the fitted model as a sum of main effects and pairwise interaction effects. Simulations showed strong recovery for both main and interaction effects when the target function is observed directly. Recovery is weaker when the target is a latent score function, but the method still approximately recovers the main effects.

The method is also applied to Polish bankruptcy data, where it provides an interpretable representation of a fitted credit-risk model. The decomposition makes it possible to inspect the effects of individual financial ratios and selected pairwise interactions, and to assess whether these effects are reasonable from an economic perspective. The results indicate that the main effects are more stable and easier to interpret than the interaction effects. Overall, the findings suggest that the proposed decomposition can provide a transparent way to use flexible machine learning models in credit-risk modelling by providing a representation that can be directly inspected and interpreted.}},
  author       = {{Kopelman, Sara}},
  issn         = {{1404-6342}},
  language     = {{eng}},
  note         = {{Student Paper}},
  series       = {{Master's Theses in Mathematical Sciences}},
  title        = {{Functional ANOVA for Inherently Interpretable Gradient Boosting in PD Modelling}},
  year         = {{2026}},
}