Customer's Relationship Segmentation Driving the Predictive Modeling for Bad Debt Events

摘要

This paper covers a comparison between two distinct approaches to neural network modeling. The first one is based on a developing of a single neural network model to predict bad debt events. The second one is based on combined models, building firstly a clustering model to recognize the pattern assigned to the customers, with a particular focus on the insolvency, and then developing several distinct neural networks to predict bad debt. In the second approach, for each group identified by the clustering model one neural network had been constructed. In that way, we turned the quite heterogeneous customer base more homogeneous, increasing the average accuracy for the predictive modeling once several straightforward models were built.