• Title/Summary/Keyword: Credit Defaulters

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The Level of Recognition, Expectation and Utilization on Policies of Social Remedies for Credit Defaulters (신용불량자의 신용불량구제정책에 관한 인지도, 기대도, 활용도)

  • Lee, Young-Hee;Lee, Seung-Sin
    • Journal of the Korean Home Economics Association
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    • v.44 no.3 s.217
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    • pp.1-11
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    • 2006
  • Although the personal credit rating has become more important than ever before in our era, a significant number of social problems have occurred due to the rising number of individuals and households with low credit ratings. The main objectives of this research are to determine effective policies of social remedies through an investigation of recognition, expectation, and utilization levels of relevant public policies available to assist individuals with low credit ratings. The sample population was taken from the credit defaulters who had visited the Credit Recovery Commission. The research was undertaken from April 28 to May 4, 2004. This study focused on the related variables concerning the degree of utilization of remedial public policies. The results showed that females, less educated individuals, and those with higher levels of expectation and recognition were more likely to utilize remedial policies. Based on the research, conclusions regarding the usage of public remedial policies for credit defaulters are as stated below. Education for households should be conducted in order to increase the expectation and recognition levels of relevant policies.

Debt-Use Intention of Young Defaulters on the Theory of Reasoned Action (20·30대 채무불이행자의 부채사용의도 : 합리적 행동이론을 중심으로)

  • Kim, Mi-Ra;Kim, Hea-Seon
    • Journal of Families and Better Life
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    • v.29 no.6
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    • pp.9-25
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    • 2011
  • This study was performed to explore the factors that affect debt-use intention of young defaulters. In addition, this study compares three models that predict the intention to use debt by young defaulters: the theory of reasoned action and two variations of it. Specifically, this study proposes an extended theory of reasoned action by introducing Ao in place of the cognitive structure in the theory of reasoned action. In addition, this study proposes Ao as an independent variable that acts on BI rather than a dependent variable. Self-administered questionnaires were completed by 196 young defaulters attending a credit management education session held by the Credit Counseling & Recovery Service in Kwang-ju, Korea. Based on the study, the conclusions are as follows: the extended theory of reasoned action as proposed in this article most suitably explained the intention to use debt by young defaulters. It was also found that young defaulters were affected by attitudes toward debt, attitudes toward using debt, and subjective norms. It is therefore suggested that an attitudinal message would change the behavior effectively for young defaulters. The findings appeared to support the usefulness of the extended theory of reasoned action and the role of Ao as an independent variable as proposed in this article to explain the intention to use debt by young defaulters. These findings have an important theoretical meaning in that they modify two existing attitude theories in the context of consumer behavior.

Incorporating BERT-based NLP and Transformer for An Ensemble Model and its Application to Personal Credit Prediction

  • Sophot Ky;Ju-Hong Lee;Kwangtek Na
    • Smart Media Journal
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    • v.13 no.4
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    • pp.9-15
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    • 2024
  • Tree-based algorithms have been the dominant methods used build a prediction model for tabular data. This also includes personal credit data. However, they are limited to compatibility with categorical and numerical data only, and also do not capture information of the relationship between other features. In this work, we proposed an ensemble model using the Transformer architecture that includes text features and harness the self-attention mechanism to tackle the feature relationships limitation. We describe a text formatter module, that converts the original tabular data into sentence data that is fed into FinBERT along with other text features. Furthermore, we employed FT-Transformer that train with the original tabular data. We evaluate this multi-modal approach with two popular tree-based algorithms known as, Random Forest and Extreme Gradient Boosting, XGBoost and TabTransformer. Our proposed method shows superior Default Recall, F1 score and AUC results across two public data sets. Our results are significant for financial institutions to reduce the risk of financial loss regarding defaulters.