Social traits and credit card default: a two-stage prediction framework

Abstract

Over the past years, studies shed light on how social norms and perceptions potentially affect loan repayments, with overtones for strategic default. Motivated by this strand of the literature, we incorporate collective social traits in predictive frameworks on credit card delinquencies. We propose the use of a two-stage framework. This allows us to segment a market into homogeneous sub-populations at the regional level in terms of social traits, which may proxy for perceptions and potentially unravelled behaviours. On these formed sub-populations, delinquency prediction models are fitted at a second stage. We apply this framework to a big dataset of 3.3 million credit card holders spread in 12 UK NUTS1 regions during the period 2015–2019. We find that segmentation based on social traits yields efficiency gains in terms of both computational and predictive performance compared to prediction in the overall population. This finding holds and is sustained in the long run for different sub-samples, lag counts, class imbalance correction or alternative clustering solutions based on individual and socio-economic attributes. Graphical abstract

Consumer Default Risk Portrait: An Intelligent Management Framework of Online Consumer Credit Default Risk

“…In recent years, an increasing number of studies have been dedicated to managing the default risk associated with online consumer loans [8]. However, most of them concentrate on developing traditional credit scoring models [9][10][11][12]. Risk management is approached as a straightforward classification problem [13].…”