• Journal of Information Technology Applications and Management
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• v.13 no.4
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• pp.273-290
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• 2006

This study suggests a frequency matrix technique to predict personal credit rate more efficiently using incomplete data sets. At first this study test on multiple discriminant analysis and logistic regression analysis for predicting personal credit rate with incomplete data sets. Missing values are predicted with mean imputation method and regression imputation method here. An artificial neural network and frequency matrix technique are also tested on their performance in predicting personal credit rating. A data set of 8,234 customers in 2004 on personal credit information of Bank A are collected for the test. The performance of frequency matrix technique is compared with that of other methods. The results from the experiments show that the performance of frequency matrix technique is superior to that of all other models such as MDA-mean, Logit-mean, MDA-regression, Logit-regression, and artificial neural networks.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.10
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• pp.3627-3641
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• 2021

Fintech, which stands for financial technology, is growing fast globally since the economic crisis hit the United States in 2008. Fintech companies are striving to secure a competitive advantage over existing financial services by providing efficient financial services utilizing the latest technologies. Fintech companies can be classified into several areas according to their business solutions. Among the Fintech sector, peer-to-peer (P2P) lending companies are leading the domestic Fintech industry. P2P lending is a method of lending funds directly to individuals or businesses without an official financial institution participating as an intermediary in the transaction. The rapid growth of P2P lending companies has now reached a level that threatens secondary financial markets. However, as the growth rate increases, so does the potential risk factor. In addition to government laws to protect and regulate P2P lending, further measures to reduce the risk of P2P lending accidents have yet to keep up with the pace of market growth. Since most P2P lenders do not implement their own credit rating system, they rely on personal credit scores provided by credit rating agencies such as the NICE credit information service in Korea. However, it is hard for P2P lending companies to figure out the intentional loan default of the borrower since most borrowers' credit scores are not excellent. This study analyzed the voices of telephone conversation between the loan consultant and the borrower in order to verify if it is applicable to determine the personal credit score. Experimental results show that the change in pitch frequency and change in voice pitch frequency can be reliably identified, and this difference can be used to predict the loan defaults or use it to determine the underlying default risk. It has also been shown that parameters extracted from sample voice data can be used as a determinant for classifying the level of personal credit ratings.

• The Journal of Information Systems
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• v.30 no.4
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• pp.203-226
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• 2021

Purpose The purpose of this study is to enhance the accuracy score of personal credit scoring using the convolutional neural networks and secure the transparency of the deep learning model using eXplainalbe Artifical Inteligence(XAI) technique. Design/methodology/approach This study built a classification model by using the convolutional neural networks(CNN) and applied a methodology that is transformation of numerical data to imaged data to apply CNN on personal credit data. Then layer-wise relevance propagation(LRP) was applied to model we constructed to find what variables are more influenced to the output value. Findings According to the empirical analysis result, this study confirmed that accuracy score by model using CNN is highest among other models using logistic regression, neural networks, and support vector machines. In addition, With the LRP that is one of the technique of XAI, variables that have a great influence on calculating the output value for each observation could be found.

• Asia-Pacific Journal of Business
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• v.11 no.3
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• pp.63-77
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• 2020

Purpose - The purpose of this study is to examine the effects of personal characteristics, loan characteristics, and interest rate characteristics of 2,653 borrowers on the delinquency possibility. In doing so, this study applies both multiple regression and logistic regression models to the data of credit unions in the city of Daegu. Design/Methodology/Approach - The major results of multiple regression analysis using SPSS are as follows. Findings - As for the results of testing the significance of the regression coefficients, it has been found that among the personal characteristics variables membership, credit rating, credit rating changes, and LTV have significant positive (+) effects on the delinquency possibility. Also it has been shown that among the loan characteristics variables loan amount, loan balance, total debt amount, collateral type, collateral amount, and repayment method have significant positive (+) effects on the delinquency possibility. Furthermore it has been found that among the interest rate characteristics variables both overdue interest rate and interest rate spread have positive (+) effects on the delinquency possibility. However, it has been shown that among the personal characteristics variables equity and membership do not have significant effects on the delinquency possibility, and that normal interest rate among the interest rate characteristics variables also do not have a significant effect on the delinquency possibility. Research Implications - By systematically analyzing the variables affecting delinquency possibility based on the results of this study, credit unions might get positive help in improving the system of managing receivables. Furthermore, the results of this study could be extended and applied to other types of financial institutions, so that financial institutions in general will also get some help to systematically manage the delinquency possibility.

• Journal of Families and Better Life
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• v.27 no.6
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• pp.95-109
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• 2009

This study focused on the development of a scale to measure the ability of consumers to use credit cards. The purposes of this study were to develop a tool which would be able to measure consumer knowledge, consumer skills and consumer attitudes. Data were collected from 313 credit card using consumers and were analyzed by employing a goodness of fit test, principal component analysis & confirmatory factor analysis(Amos 5.0), multiple regression. The results from this study were as follows: 1) Six factors of consumer knowledge(16-items) were identified: damage salvation; credit delinquency; personal credit information; credit provision period; credit & credit card issuance; credit delinquent striking out a record & credit rating. The total variance was 55.86%. 2) Three factors of consumer skills(17-items) were identified: credit delinquency & over-consumption; credit card management; and loss & damage salvation. The total variance was 62.90%. 3) Three factors of consumer attitudes(16-items) were identified: credit delinquency & credit; credit card issuance & use; and credit card management. The total variance was 58.75%.

• 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.

• Journal of Science and Technology Studies
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• v.19 no.1
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• pp.91-135
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• 2019

As we live a life increasingly mediated by computers, we often outsource our critical judgments to artificial intelligence(AI)-based algorithms. Most of us have become quite dependent upon algorithms: computers are now recommending what we see, what we buy, and who we befriend with. What happens to our lives and identities when we use statistical models, algorithms, AI, to make a decision for us? This paper is a preliminary attempt to chronicle a historical trajectory of judging people's economic and moral worth, namely the history of credit-rating within the context of the history of capitalism. More importantly this paper will critically review the history of credit-rating from its earlier conception to the age of big data and algorithmic evaluation, in order to ask questions about what the political implications of outsourcing our judgments to computer models and artificial intelligence would be. Some of the questions I would like to ask in this paper are: by whom and for what purposes is the computer and artificial intelligence encroached into the area of judging people's economic and moral worth? In what ways does the evolution of capitalism constitute a new mode of judging people's financial and personal identity, namely the rated self? What happens in our self-conception and identity when we are increasingly classified, evaluated, and judged by computer models and artificial intelligence? This paper ends with a brief discussion on the political implications of the outsourcing of human judgment to artificial intelligence, and some of the analytic frameworks for further political actions.

• Journal of Industrial Convergence
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• v.21 no.6
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• pp.1-11
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• 2023

The purpose of this study is to verify the factors affecting corporate bankruptcy by using non-financial information of companies invested by credit guarantee institutions. In this study, 594 companies (525 normal companies, 69 insolvent companies) invested in by the Korea Credit Guarantee Fund from March 2014 to the end of December 2022 were selected as samples. Non-financial information of companies was divided into founder characteristics information, company characteristics information, and corporate investment information, and cross-analysis and logistic regression analysis were conducted. As a result of the cross-analysis, personal credit rating, industry, and joint investment were selected as significant variables, and logistic regression analysis was conducted for those variables, and two variables, personal credit rating and joint investment, were selected as important factors for bankruptcy. In business management, the founder's personal credit and the importance of joint investment in investment support were found out. It will help to minimize bankruptcy if institutions that support investment in SMEs reflect these results in their screening and systematically build cooperative relationships with private investment institutions. It is hoped that this study will provide an opportunity to pay more attention to the factors that affect the bankruptcy of companies that receive direct investment from public institutions.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.5
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• pp.77-90
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• 2023

This study aims to investigate the potential of alternative credit assessment through Social Networking Sites (SNS) as a complementary tool to conventional loan review processes. It seeks to discern the impact of SNS usage characteristics and loan product attributes on credit loan repayment. To achieve this objective, we conducted a binomial logistic regression analysis examining the influence of SNS usage patterns, loan characteristics, and personal attributes on credit loan conditions, utilizing data from Company A's credit loan program, which integrates SNS data into its actual loan review processes. Our findings reveal several noteworthy insights. Firstly, with respect to profile photos that reflect users' personalities and individual characteristics, individuals who choose to upload photos directly connected to their personal lives, such as images of themselves, their private circles (e.g., family and friends), and photos depicting social activities like hobbies, which tend to be favored by individuals with extroverted tendencies, as well as character and humor-themed photos, which are typically favored by individuals with conscientious traits, demonstrate a higher propensity for diligently repaying credit loans. Conversely, the utilization of photos like landscapes or images concealing one's identity did not exhibit a statistically significant causal relationship with loan repayment. Furthermore, a positive correlation was observed between the extent of SNS usage and the likelihood of loan repayment. However, the level of SNS interaction did not exert a significant effect on the probability of loan repayment. This observation may be attributed to the passive nature of the interaction variable, which primarily involves expressing sympathy for other users' comments rather than generating original content. The study also unveiled the statistical significance of loan duration and the number of loans, representing key characteristics of loan portfolios, in influencing credit loan repayment. This underscores the importance of considering loan duration and the quantity of loans as crucial determinants in the design of microcredit products. Among the personal characteristic variables examined, only gender emerged as a significant factor. This implies that the loan program scrutinized in this analysis does not exhibit substantial discrimination based on age and credit scores, as its customer base predominantly consists of individuals in their twenties and thirties with low credit scores, who encounter challenges in securing loans from traditional financial institutions. This research stands out from prior studies by empirically exploring the relationship between SNS usage and credit loan repayment while incorporating variables not typically addressed in existing credit rating research, such as profile pictures. It underscores the significance of harnessing subjective, unstructured information from SNS for loan screening, offering the potential to mitigate the financial disadvantages faced by borrowers with low credit scores or those ensnared in short-term liquidity constraints due to limited credit history a group often referred to as "thin filers." By utilizing such information, these individuals can potentially reduce their credit costs, whereas they are supposed to accrue a more substantial financial history through credit transactions under conventional credit assessment system.

• Information Systems Review
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• v.23 no.1
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• pp.69-91
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• 2021

Credit Bureaus in Korea commonly use financial transaction information of the past and present time for calculating an individual's credit scores. Compared to other rating factors, the repayment history information accounts for a larger weights on credit scores. Accordingly, despite full redemption of overdue payments, late payment history is reflected negatively for the assessment of credit scores for certain period of the time. An individual with debt delinquency can be classified into two groups; (1) the individuals who have faithfully paid off theirs overdue debts(Normal Repayment), and (2) those who have not and as differences of creditworthiness between these two groups do exist, it needs to grant relatively higher credit scores to the former individuals with normal repayment. This study is designed to analyze the factors of normal repayment of Korean financial debt delinquents based on credit information of personal loan, overdue payments, redemption from Korea Credit Information Services. As a result of the analysis, the number of overdue and the type of personal loan and delinquency were identified as significant variables affecting normal repayment and among applied methodologies, neural network models suggested the highest classification accuracy. The findings of this study are expected to improve the performance of individual credit scoring model by identifying the factors affecting normal repayment of a financial debt delinquent.