A Quadratic Programming Solution to the FICO Credit Scoring Problem

TL;DR

This paper presents an exact quadratic programming approach to solve the FICO Credit Scoring Problem, enabling the development of more accurate and interpretable credit scoring models while satisfying essential constraints.

Contribution

It introduces a novel quadratic programming formulation that provides an exact solution to the FICO Credit Scoring Problem, improving upon previous approximate methods.

Findings

Quadratic programming effectively handles PILE constraints.

The method achieves exact maximization of divergence.

FICO's existing credit score development benefits from this technology.

Abstract

After decades of experience in developing credit scores, the FICO corporation has formulated the FICO Credit Scoring Problem as follows: Find the Generalized Additive Model (GAM), with component step functions, that maximizes divergence subject to the PILE (Palatability, Interpretability, Legal, Explain-ability) constraints. The PILE constraints are also called shape constraints, and satisfying them is called score engineering. Before 2003, FICO used an algorithm, based on Linear Programing, to approximately solve the FICO Credit Scoring Problem. In this paper, I develop an exact solution to the FICO Credit Scoring Problem. Finding the exact solution has eluded FICO for years. Divergence is a ratio of quadratic functions of the score weights. I show that the max divergence problem can be transformed into a quadratic program. The quadratic programming formulation allows one to handle the PILE constraints very easily. FICO currently uses aspects of this technology to develop the famous FICO Credit Score.