A Data Mining framework to model Consumer Indebtedness with Psychological Factors

TL;DR

This paper presents a data mining framework that incorporates psychological factors, such as impulsivity, to improve the modeling of consumer indebtedness, highlighting the importance of psychological traits in financial behavior analysis.

Contribution

It introduces a novel approach combining data mining with psychological factors to better understand and model consumer debt behavior.

Findings

Psychological factors significantly improve debt modeling accuracy.

Impulsivity is a key predictor of consumer indebtedness.

The framework offers a new perspective for analyzing consumer debt.

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.