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