Abstract
In credit risk estimation, the most important element is obtaining a probability of default as close as possible to the effective risk. This effort quickly prompted new, powerful algorithms that reach a far higher accuracy, but at the cost of losing intelligibility, such as Gradient Boosting or ensemble methods. These models are usually referred to as “black-boxes”, implying that you know the inputs and the output, but there is little way to understand what is going on under the hood. As a response to that, we have seen several different Explainable AI models flourish in recent years, with the aim of letting the user see why the black-box gave a certain output. In this context, we evaluate two very popular eXplainable AI (XAI) models in their ability to discriminate observations into groups, through the application of both unsupervised and predictive modeling to the weights these XAI models assign to features locally. The evaluation is carried out on real Small and Medium Enterprises data, obtained from official italian repositories, and may form the basis for the employment of such XAI models for post-processing features extraction.
Highlights
Probability of default (PD) estimation is an issue which banks and other financial institutions have been confronting with since the dawn of credit
We worked on data encompassing the last 6 years, comprising more than 2 millions SME observations, we kept all the defaulted cases and, for the not-defaulted ones, we randomly sampled a group of observation to maintain as they were, while with the remaining we built 5,000 clusters per year and employed the medoids as input observations
Either for SHAP or LIME, the number of clusters which maximizes the silhouette score is two, coherently with the problem at hand; we can see this by looking at the silhouette scores represented by the vertical red dashed line, which is higher for the plot with two clusters, and from the part of the clusters who enter the X axis negative score, which increase as we increase the number of clusters
Summary
Probability of default (PD) estimation is an issue which banks and other financial institutions have been confronting with since the dawn of credit. The increase in prediction power of new algorithms, though, takes a toll on explainability, since the models are so complex that it is close to impossible to establish clear links between the inner workings of the model and the given output. This surely represents a problem and hinders their diffusion, other than raising a series of ethical and regulamentary issues, which are starting to be addressed (see, for example European Commission (2020))
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