Credit Consumption and Financial Risk Among Danish Households— A Register-Based Study of the Distribution of Bank and Credit Card Debt

e18552 Background: Cancer is the 2nd most common cause of death in the country, eclipsed only by heart disease. Cancer care is increasingly characterized by financial toxicity related to high-cost treatments, though it is unknown whether other chronic conditions impose similar financial harms. Methods: We conducted a retrospective analysis of the Health and Retirement Study participants interviewed between 2012-2018. This is a national, longitudinal survey conducted every two years of adults 50 and older and their spouses. We used fixed effect regression models to compare changes in financial debt among households with new diagnosis of cancer, other major chronic conditions (diabetes, stroke, or heart disease), and no new health diagnosis (or health shock). Since more affluent households may respond to health shocks differently, we estimated separate comparisons for households above versus below median wealth in 2012, prior to new health conditions. We assessed use of any non-housing financial debt, credit card debt, and home equity lines of credit among the subset of homeowning households. Results: In this study of 14,153 households, average age at interview was 62 years, with 43% male, 70% White, 22% Black, 13% Hispanic, and 70% with up to high school education. Of this population, 25% held credit card debt, 70% owned a home, 18% had a home equity line of credit, and 9% used a home equity line of credit. Among households with below median wealth when they entered the study in 2012 ( < $23,000 in $2016), a new cancer diagnosis was associated with a 4.7 percentage point increase in financial debt (12.5% effect size, p < 0.05). Participants diagnosed with a chronic condition (heart condition, stroke or diabetes) were 3.6 percentage points more likely to develop financial debt (9.6%, p < 0.05) compared to households that did not develop a new chronic condition. Such differences were eliminated in participants in a house with above median wealth. There was no difference in credit card debt, availability of home equity line of credit, or use of home equity line of credit for participants with a new diagnosis. Conclusions: New diagnosis of cancer or a chronic condition were associated with increased financial debt for older Americans living in a household that were below median wealth.

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Psychological and demographic factors influencing responsible credit card debt payment

Journal of Financial Services Marketing ◽

10.1057/s41264-021-00094-0 ◽

2021 ◽

Author(s):

Asli Elif Aydin

Keyword(s):

Credit Card ◽

Demographic Factors ◽

Credit Card Debt ◽

Factors Influencing ◽

Debt Payment

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Evade from Credit Card Debt Consolidation, Overdue, Bills & Credit Card Debt.

SciVee ◽

10.4016/34690.01 ◽

2011 ◽

Cited By ~ 3

Keyword(s):

Credit Card ◽

Credit Card Debt

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Credit Card Debt: Beware the Plastic Loan Shark

Expressing America: A Critique of the Global Credit Card Society Expressing America: A critique of the global credit card society ◽

10.4135/9781452243115.n3 ◽

2012 ◽

pp. 59-82

Keyword(s):

Credit Card ◽

Credit Card Debt ◽

Loan Shark

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Determinants of credit card spending and debt of Chinese consumers

International Journal of Bank Marketing ◽

10.1108/ijbm-01-2018-0010 ◽

2019 ◽

Vol 37 (2) ◽

pp. 545-564 ◽

Cited By ~ 5

Author(s):

Liqiong Lin ◽

Mohamad Dian Revindo ◽

Christopher Gan ◽

David A. Cohen

Keyword(s):

Rapid Growth ◽

Credit Card ◽

Credit Cards ◽

Demographic Characteristics ◽

Chinese Government ◽

Credit Card Debt ◽

Chinese Consumers ◽

Content Type ◽

Positive Effects ◽

Credit Card Use

PurposeThe rapid growth of credit card use in China poses the potential for card overuse and the accumulation of increased debt. The purpose of this paper is to report on an investigation into the determinants of overall credit card spending and card-financed debt by Chinese consumers.Design/methodology/approachThis study focusses on two dependent variables: credit card monthly spending and card debt. The spending measure is based on consumer outlay for the month preceding the survey. Card debt is the consumers’ outstanding credit card debt when the survey was conducted. Three groups of independent measures are used: socio-demographic characteristics, card features and consumer attitude towards money. Both card spending and card debt are estimated with OLS methods. Data was obtained from the 2013 China Household Finance Survey of 1,920 households in 29 provinces and 262 counties across China that used credit cards over the survey period.FindingsThe empirical findings suggest consumers’ attitude towards money is more important in explaining card spending and debt variation than socio-demographic characteristics and card features. The credit limit set for a card, obligations to other loans and the method of paying for ordinary shopping exhibit positive effects on both card spending and card debt, while age exhibits a negative effect. Further, card spending is positively correlated with card debts, but the factors that determine card spending do not necessarily affect card debt and vice versa. Minimum card debt payments, cash advances, card tenure and interest-bearing debt have no effect on card spending but have positive effects on card debt. In addition, gender and income have opposite effects on card spending and debt.Practical implicationsThe relationships we have documented suggest several actions the Chinese Government could consider dealing with credit card debt risk. Controlling the aggressive promotional campaigns that card issuers use to attract consumers and aggressive credit policies should be a focus of attention. The Chinese Government might, for example, impose minimum age and income requirements for granting credit cards and prohibit issuance of new cards to applicants who are already in debt with other types of credit. In addition, more stringent criteria to curb increases in card limits and tighter control over cash advances made on cards should be applied. Minimum payment amounts can also be increased in order to reduce credit card debt risk.Originality/valueDespite ample documentation of consumers’ credit card behaviour, the literature is deficient in at least two areas of enquiry. First, most previous research has investigated either credit card spending behaviour or card debt, but not both. Second, with few exceptions, most research has investigated a range of specific factors that affect credit card use. In contrast, this study investigates card spending as well as card debt behaviour using a wide variety of consumer dimensions particularly relevant to credit card use and resulting debt. In addition, this study focusses on Chinese consumers, who traditionally prefer to save first and delay spending. The impact of the rapid growth of credit card use on this traditional Chinese orientation towards spending is dynamic. Documenting the influence of the individual factors examined in this study is likely to be of value to both policy makers and institutions that offer and manage credit in this changing environment.

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Buying Delinquent Credit Card Debt

SuperCash ◽

10.1002/9781119201519.ch3 ◽

2015 ◽

pp. 47-54

Keyword(s):

Credit Card ◽

Credit Card Debt

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Improved ML-Based Technique for Credit Card Scoring in Internet Financial Risk Control

Complexity ◽

10.1155/2020/8706285 ◽

2020 ◽

Vol 2020 ◽

pp. 1-14

Author(s):

Shuangshuang Fan ◽

Yanbo Shen ◽

Shengnan Peng

Keyword(s):

Machine Learning ◽

Credit Risk ◽

Credit Card ◽

Financial Risk ◽

Risk Control ◽

Assessment Model ◽

Complex Data ◽

Continuous Growth ◽

Scoring Model ◽

Extreme Gradient Boosting

With the rapid development of China’s Internet finance industry and the continuous growth of transaction amount in recent years, a variety of financial risks have increased, especially credit risk in the financial industry. Also, the credit risk evaluation is usually made by using the application card scoring model, which has the shortcomings of strict data assumption and inability to process complex data. In order to overcome the limitations of the credit card scoring model and evaluate credit risk better, this paper proposes a credit evaluation model based on extreme gradient boosting tree (XGBoost) machine learning (ML) algorithm to construct a credit risk assessment model for Internet financial institutions. At the same time, an Internet lending company in China is taken as a case study to compare the performance of the traditional credit card scoring model and the proposed machine learning (ML) algorithm model. The results show that ML algorithm has a very significant advantage in the field of Internet financial risk control, it has more accurate prediction results and has no particularly strict assumptions and restrictions on data, and the process of processing data is more convenient and reliable. We should increase the application of ML in the field of financial risk control. The value of this paper lies in enriching the related research of financial technology and providing a new reference for the practice of financial risk control.

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