Weight of evidence transformation in credit scoring models: How does it affect the discriminatory power?

Sammanfattning: Weight of evidence (WOE) transformation has been used for several decades in the credit industry. However, despite its widespread use, it has, surprisingly, been an overlooked approach in published literature. In this paper, we, therefore, investigate what effect WOE transformation has on the discriminatory power of a credit-scoring model. Our results suggest that using WOE transformation with logistic regression decreased the discriminatory power across a majority of the evaluation metrics compared to the models that did not use WOE transformed variables. Moreover, using an information value for variable selection did not provide any benefits over using the backward selection technique. However, applying support vector machine, we found mixed results depending on the preferred evaluation metric. Using an information value seems to provide some benefits regarding variable selection compared to the recursive feature elimination technique.