  Traditional credit scorecards are generalized additive models (GAMs) with
step functions as the component functions. The shapes of the step functions may
be constrained in order to satisfy the PILE (Palatability, Interpretability,
Legal, Explain-ability) constraints. Before 2003, FICO used Linear Programming
to find the traditional scorecard that approximately maximizes divergence
subject to the PILE constraints. In this paper, I introduce the Liquid
Scorecard, that allows the component functions to be, at least partially,
smooth curves. I use Quadratic Programming and B-Spline theory to find the
Liquid Scorecard that exactly maximizes divergence subject to the PILE
constraints. FICO uses aspects of this technology to develop the famous FICO
Credit Score.
