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Predicting Credit Card Delinquency: A Fundamental Model of Cardholder Financial Behavior

Huse, Håvard
Doctoral thesis
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URI
https://hdl.handle.net/11250/2640771
Date
2019-11
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  • Doctoral Dissertations (SOL) [76]
Abstract
This thesis proposes a model of credit card customer delinquency based on

theoretical advancements in financial decision-making. As follows, this thesis

has two main research purposes.

First, credit card delinquency is modeled explicitly, incorporating mechanisms

from mental accounting and financial decision-making. This allows

for more realistic modeling of cardholder behavior, while simultaneously inspecting

the validity of these theoretical concepts.

Second, the modeling specification advances previous research in the

behavior scoring literature. Accounting for individual-level heterogeneity,

dynamic effects are assigned as individual lag weights using a segmented

approach. Hence, potentially different behavioral patterns between non-delinquent

and eventual delinquent cardholders are modeled directly.

Using a comprehensive dataset combining credit and debit transactions

of cardholders between June 2008 and June 2011 from a Norwegian bank,

support is found for the following three hypotheses related to mental accounting

and present bias. First, increased payment decoupling leads to a higher

likelihood of delinquency, when continued borrowing is promoted by reduced

salience of past expenses. Second, the results show that behavior consistent

with persistence of decision-making ineptitude also increases the likelihood

of delinquency. Some cardholders habitually spend excessively, refusing to

accommodate consumption to a financially reasonable level. Third, a lower

concern for future consequences also increases the likelihood of delinquency.

Present-biased individuals tend to discount future credit card repayments at

a higher rate and consistently spend at perilously high rates.

Further, the results reveal how the structure of dynamic effects improves

prediction of delinquency. Capturing the heterogeneous effects of previous

financial status leads to a more precise understanding of cardholder behavior.

The proposed model has greater predictive performance than machine

learning algorithms that are frequently applied to credit scoring data.

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