A Data Mining Framework to Model Consumer Indebtedness with Psychological Factors

J. Garibaldi, E. Ferguson, U. Aickelin
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引用次数: 12

Abstract

Modelling Consumer Indebtedness has proven to be a problem of complex nature. In this work we utilise Data Mining techniques and methods to explore the multifaceted aspect of Consumer Indebtedness by examining the contribution of Psychological Factors, like Impulsivity to the analysis of Consumer Debt. Our results confirm the beneficial impact of Psychological Factors in modelling Consumer Indebtedness and suggest a new approach in analysing Consumer Debt, that would take into consideration more Psychological characteristics of consumers and adopt techniques and practices from Data Mining.
基于心理因素的消费者负债数据挖掘框架
消费者负债建模已被证明是一个复杂的问题。在这项工作中,我们利用数据挖掘技术和方法,通过检查心理因素(如冲动性)对消费者债务分析的贡献,探索消费者债务的多方面。我们的研究结果证实了心理因素对消费者负债建模的有益影响,并提出了一种分析消费者债务的新方法,该方法将更多地考虑消费者的心理特征,并采用数据挖掘的技术和实践。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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