An Integrated Framework on Human-in-the-Loop Risk Analytics

Abstract

Risk analytics is an integral component in the overall assessment of the risk profile for potential and existing obligors. For example, credit worthiness is often assessed via the use of scorecards, which are regulatory credit risk models developed based on historical data and domain expertise in banks and financial institutions. A pure statistical model, however, often fails to entertain regulatory requirements on both predictiveness and interpretability at the same time. Instead, practical risk models are developed by incorporating expert opinions within the development process, such as forcing the direction of travel for certain financial factors. In this article, the author proposes a unified framework, termed constrained and partially regularized logistic regression (CPR-LR) model, on how human inputs could be embedded in the statistical estimation procedure when developing credit risk models. By expressing such inputs as model constraints at different levels, the proposed approach serves as an effective solution to developing intuitive, easy-to-interpret, and statistically robust credit risk models, as demonstrated in the author’s experiments. This work also contributes to the growing field of human-in-the-loop model development, in which the author shows that domain expertise can be formulated as model constraints, thus biasing the resulting statistical model to be more interpretable and regulation compliant.