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

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

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: [Figure not available: see fulltext.]

Clustering, Credit card default, Prediction, Social traits
0254-5330
Gaganis, Chrysovalantis
a66db976-d7d1-4a85-8057-d2cde46f0d33
Papadimitri, Panagiota
b7edf14e-3b00-4317-a6ce-8741b593d5b0
Pasiouras, Fotios
48097419-7f9d-4bd4-93a0-6ae59bdf9a0e
Tasiou, Menelaos
e402686d-dbf2-48d5-aba8-da9b4ad3701f
Gaganis, Chrysovalantis
a66db976-d7d1-4a85-8057-d2cde46f0d33
Papadimitri, Panagiota
b7edf14e-3b00-4317-a6ce-8741b593d5b0
Pasiouras, Fotios
48097419-7f9d-4bd4-93a0-6ae59bdf9a0e
Tasiou, Menelaos
e402686d-dbf2-48d5-aba8-da9b4ad3701f

Gaganis, Chrysovalantis, Papadimitri, Panagiota, Pasiouras, Fotios and Tasiou, Menelaos (2022) Social traits and credit card default: a two-stage prediction framework. Annals of Operations Research. (doi:10.1007/s10479-022-04859-1).

Record type: Article

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: [Figure not available: see fulltext.]

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s10479-022-04859-1 - Version of Record
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More information

Accepted/In Press date: 23 June 2022
e-pub ahead of print date: 18 July 2022
Additional Information: Publisher Copyright: © 2022, The Author(s).
Keywords: Clustering, Credit card default, Prediction, Social traits

Identifiers

Local EPrints ID: 469679
URI: http://eprints.soton.ac.uk/id/eprint/469679
ISSN: 0254-5330
PURE UUID: 7d83e4be-6d77-4635-9989-ccb0574fa5f9
ORCID for Panagiota Papadimitri: ORCID iD orcid.org/0000-0002-1857-6097

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Date deposited: 22 Sep 2022 16:34
Last modified: 06 Jun 2024 02:14

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Contributors

Author: Chrysovalantis Gaganis
Author: Panagiota Papadimitri ORCID iD
Author: Fotios Pasiouras
Author: Menelaos Tasiou

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