Collaborating Authors


Evolution of Credit Risk Using a Personalized Pagerank Algorithm for Multilayer Networks Machine Learning

In this paper we present a novel algorithm to study the evolution of credit risk across complex multilayer networks. Pagerank-like algorithms allow for the propagation of an influence variable across single networks, and allow quantifying the risk single entities (nodes) are subject to given the connection they have to other nodes in the network. Multilayer networks, on the other hand, are networks where subset of nodes can be associated to a unique set (layer), and where edges connect elements either intra or inter networks. Our personalized PageRank algorithm for multilayer networks allows for quantifying how credit risk evolves across time and propagates through these networks. By using bipartite networks in each layer, we can quantify the risk of various components, not only the loans. We test our method in an agricultural lending dataset, and our results show how default risk is a challenging phenomenon that propagates and evolves through the network across time.

Towards Global Explanations for Credit Risk Scoring Machine Learning

In this paper we propose a method to obtain global explanations for trained black-box classifiers by sampling their decision function to learn alternative interpretable models. The envisaged approach provides a unified solution to approximate non-linear decision boundaries with simpler classifiers while retaining the original classification accuracy. We use a private residential mortgage default dataset as a use case to illustrate the feasibility of this approach to ensure the decomposability of attributes during pre-processing.

Machine Learning Applications in Credit Risk


Typical decisions: • Grant credit/not to new applicants • Increasing/Decreasing spending limits • Increasing/Decreasing lending rates • What new products can be given to existing applicants? Step 2: Assign every entity to its closest medoid (using the distance matrix we have calculated). Step 3: For each cluster, identify the observation that would yield the lowest average distance if it were to be re-assigned as the medoid. If so, make this observation the new medoid. Step 4: If at least one medoid has changes, return to step 2. Otherwise, end the algorithm.