Metric Divergence Measures and Information Value in Credit Scoring

Abstract EN

Recently, a series of divergence measures have emerged from information theory and statistics and numerous inequalities have been established among them.

However, none of them are a metric in topology.

In this paper, we propose a class of metric divergence measures, namely, Lp(P∥Q), P≥1, and study their mathematical properties.

We then study an important divergence measure widely used in credit scoring, called information value.

In particular, we explore the mathematical reasoning of weight of evidence and suggest a better alternative to weight of evidence.

Finally, we propose using Lp(P∥Q) as alternatives to information value to overcome its disadvantages.