Abstract:
This paper proposes a conceptual modeling framework based on category theory that serves as a tool to study common structures underlying diverse approaches to modeling credit default that at first sight may appear to have nothing in common. The framework forms the basis for an entropy-based stacking model to address issues of inconsistency and bias in classification performance. Based on the Lending Club's peer-to-peer loans dataset and Taiwanese credit card clients dataset, relative to individual base models, the proposed entropy-based stacking model provides more consistent performance across multiple data environments and less biased performance in terms of default classification. The process itself is agnostic to the base models selected and its performance superior, regardless of the models selected.
