Building Intelligent Credit Scoring Systems Using Decision Tables

Accuracy and comprehensibility are two important criteria when developing decision support systems for credit scoring. In this paper, we focus on the second criterion and propose the use of decision tables as an alternative knowledge visualisation formalism which lends itself very well to building intelligent and user-friendly credit scoring systems. Starting from a set of propositional if-then rules extracted by a neural network rule extraction algorithm, we construct decision tables and demonstrate their efficiency and user-friendliness for two real-life credit scoring cases.

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