Payment Default Research Articles

Default in Payment in Land Sales That Have Been Registered at the Land Office (A Study of Court Decision Number 15/Pdt/2022/PT.Bdg.)

This research discusses default in the payment of land purchase using a home ownership credit as a basis for canceling a sale that has been registered at the land office, based on the Court Decision Number 15/Pdt/2020/PT.Bdg. In this case, the bank disbursed the credit according to the credit agreement between the buyer and the bank, which should have been paid to the seller by the buyer. However, since the money was not paid despite the transfer of rights being registered at the Land Office, the seller suffered a loss due to the buyer's default. This default can create new legal problems if not promptly resolved through the court. The research method used is doctrinal legal research, based on legal doctrines and applicable laws and regulations. The research typology used is descriptive-analytical. This method describes the applicable laws and regulations and relates them to legal theories and the practice of positive law relevant to the issue. This research focuses on whether a default can annul a land sale that has been registered with the land office and the differences between default and unlawful acts in canceling an engagement based on applicable regulations. The result of this research is that the transfer of land can be annulled by proving that a default has indeed occurred through a judge's decision in accordance with Article 1266 of the Civil Code.

Predicting determinants of industrial firms’ loan payment default: Logit model versus probit model

Appropriate control and management of credit risk has become the main concern of financial institutions, which are constantly developing models for analyzing, assessing and predicting this risk, particularly in view of the prudential standards required by central banks. The current research paper aims to specify the determinants of payment defaults in Tunisian companies in the industrial sector so as to develop a model with the optimal predictive precision of forecasting payment default when it relates to industrial firms functioning in the Tunisian environment. Within this framework, we compared two generalized linear models which correspond to the logit and probit models based on a sample of Tunisian industrial companies. The results revealed that debt ratio is the most important variable that increases the probability of default payment, while the solvency ratio reduces the probability of default payment. This study highlights the vital contribution of macroeconomic agents, such as GDP growth rate and inflation rate, in terms of the prediction of default payment.

Research on credit card default repayment prediction model

This study compares the predictive ability of various machine learning models for credit card default repayment within different prediction frameworks, using data from a commercial bank in China. Firstly, utilizing different tree models, we explore the impact on post-default repayment of different factors. Next, a split-sample time series prediction is carried out with two neural network algorithms, BPNN and ELM. The outcomes indicate that, ELM yields a significantly superior prediction performance compared to the BPNN model. Thirdly, the predictive performances of ten machine learning models are compared using full-sample data. The findings demonstrate that XGBoost and ELM models have superior predictive performances in full-sample analyses. Fourthly, this study employs the EMD data decomposition technique to examine the predictive ability of the XGBoost and ELM models in various frequency data. The results indicate that the predictive efficacy may differ depending on the frequency and repayment period after default. The findings are valuable for commercial banks in developing a framework and selecting a methodology to address the challenge of predicting credit card default payments.

The Impact of The Sovereign Credit Rating on The Possibility of Financing External Loans to The Iraqi Economy

External loans are a source of credit revenue. The process of obtaining it requires determining the degree of credit risk by predicting the financial solvency of the economy to judge its financial ability to pay the principal and interest dues of the loan at the specified maturity date. Considering that ability is a determinant of the creditworthiness of his economy, the extent to which financing can continue with this external credit source and its cost. The government of Iraq faced the difficulty of financing external loans from the international capital markets with its high return due to the lack of a sovereign credit rating that measures the risk of default through a process to determine the degree of that risk for investors of external lending funds. Therefore, the research came to show the effect of determining the sovereign credit rating on the extent to which the Government of Iraq can finance foreign loans, whether from capital markets or foreign governments and the impact of this classification on the credit facilities granted. The research concluded that the economy of Iraq got a sovereign credit rating within the degree of creditworthiness according to the classification of speculation to reflect the possibility of default in payment due to the dependence of the financial solvency of its economy on oil revenues, as the credit risks rise due to the inability to pay in the event of a decline in prices in the foreign market. However, relative stability within the short term enabled the Iraqi government to obtain foreign loans, with the requirement that Iraq provide guarantees by foreign governments and institutions in exchange for this source of credit.

The Effect of Over-Confidence on Jiwasraya's 2017-2020 Payment Defaults

Overconfidence is a term that refers to the human tendency to estimate their abilities, knowledge, or results with more confidence than they should. PT. Jiwasraya Insurance (Persero), the oldest insurance company in Indonesia which is owned by BUMN, was declared to have failed to pay its customers' policies in 2020. This failure to pay was caused by errors in managing customer funds, namely by collecting funds for JS Products. Saving Plan and investing with customer funds in high risk stocks. The purpose of this research is to find out why Jiwasraya experienced payment failure. Therefore, the variable prices of high-risk shares purchased by companies from 2017 to 2020 is used as a secondary variable. Secondary data was obtained from the Indonesian Stock Exchange and the Financial Services Authority. This study uses. Vector Auto regressive (VAR) analysis technique with Stata/IC 15.0 software. The research results show that it is the Jiwasraya company's share price that influences Jiwasraya's shares in the short term. In the long term, it shows that the share price of Semen Baturaja does not have a significant effect on Jiwasraya's share price. Meanwhile, the share price of the West Java Development Bank has a significant negative effect on Jiwasraya's share price. Then, PP Properti's share price has a significant positive effect on Jiwasraya's share price. The results of this research can be used as consideration for companies and investors in making investment decisions so as not to take high-risk actions because of too high self-confidence.

MACHINE LEARNING-BASED APPROACHES FOR CREDIT CARD DEBT PREDICTION

The primary concern in the stock market and banks that offer credit cards has been a problem over time. Regardless of their capacity to pay, most card users abuse their credit cards and accrue debt from cash cards. The most significant issue facing cardholders and banks alike is this calamity. Predicting credit card customers' default payments became vital to lowering this risk. Data mining approaches, including decision tree, logistic regression, and Naïve Bayes with feature selection methods, were applied to secondary credit card debt data to identify the significant factors that impact credit card default and to enhance the prediction of credit card default. As a result, the decision tree with Gini index splitting criteria forward selection wrapper method was identified as the best model with the highest percentages of accuracy, precision, sensitivity, and area under ROC of 76.39%, 72.02%, 85.08%, and 0.891 respectively. Additionally, the significant factors that impact credit card default are gender, education level, repayment status in July 2005, repayment status in August 2005, status of repayment in September 2005, and the amount paid in June 2005 and May 2005. This study may help financial institutions assess creditworthiness and give consumers insights into their financial behaviors.

Educational cooperation in the perspective of tripartite evolutionary game among government, enterprises and universities.

Government-enterprise-university synergy (GEUS) is an effective way to mobilize government, enterprises, and universities to collaborate on education, but these three parties involved in GEUS may, out of bounded rationality, choose to collaborate in ways that benefit themselves and harm others. To guide the three parties to better cooperation, this study creates an evolutionary game model among the three parties and evaluates the applicability and validity of the model by selecting the educational cooperation data in Beijing. It is shown that participation in education cooperation is the best course of action for all three parties. The intensity of willingness to participate in the GEUS is on the order of high to low for universities, enterprises, and the government. If the three parties wish to accomplish education collaboration sooner, they can increase default payments, boost government revenues, raise corporate participation in distribution, and reduce government and government spending. These results highlight the inherent regularities of GEUS and provide concrete implementation strategies to improve the efficiency of education cooperation.

Detecting Frauds and Payment Defaults on Credit Card Data Inherited With Imbalanced Class Distribution and Overlapping Class Problems: A Systematic Review

Detecting Frauds and Payment Defaults on Credit Card Data Inherited With Imbalanced Class Distribution and Overlapping Class Problems: A Systematic Review

TruChit: A Blockchain-Based Trusted Chit Fund System with Creditworthiness Evaluation

Chit fund is a peer-to-peer saving and borrowing scheme operated among trusted groups of people. It is a reliable source of funds in emergencies with no guarantor and low-interest rate. Despite their enduring benefits, chit funds face challenges related to trust, transparency, and security. At present, unscrupulous subscribers might join chit-funds, borrow money, and make payment defaults. This leads to disruptions in contribution cycle and affects overall functioning. Additionally, non-transparent record-keeping and transaction processes, hinder participants from verifying fund activities, creating a susceptible environment for fraud and malpractice. These challenges are an obstacle to the sustainability and trustworthiness of the chit-fund system. Therefore, to overcome aforementioned challenges, this paper proposes TruChit, a blockchain-based chit fund system with creditworthiness evaluation framework. It leverages the Adaptive Neuro-Fuzzy Inference System (ANFIS) to assess the creditworthiness of subscribers, which ensures the credibility and reliability of individual participants. Further, proposes permissioned blockchain-assisted chit-fund framework with role-based access control to instill trust, security, and transparency within chit-fund operations. Moreover, the efficacy of TruChit is evaluated by analyzing credit score dataset and achieves 93.5% accuracy, which is better than other approaches.

Bond yield effects of corporate bond default: Evidence from bond default events of 2014–2022

This paper conducts an event study to examine the impact of bond default events on daily bond yields following rigid payment defaults. Our results demonstrate a significant increase in abnormal returns of corporate bonds subsequent to intra-industry bond defaults. Notably, the extent of this effect varies based on credit quality and bond type. Further analyses reveal that companies of lower credit quality and unlisted are notably more affected by default events. Specifically, we find some evidence that defaults in publicly listed firms, state-owned enterprises, or medium-term notes lead to more pronounced effects on bond yields.

UPAYA HUKUM PENYELESAIAN WANPRESTASI DALAM PERJANJIAN SEWA MENYEWA KENDARAAN

This journal looks at the legal options available to settle cases involving late payments, such as those involving car rentals and rental contracts. This study's primary goal is to examine the different default settlement options and take into account potential obstacles and solutions for the parties concerned. A combination of empirical research and normative legal analysis is one of the research methodologies employed. This information was gathered by reading up on relevant laws and regulations, reviewing legal literature, and examining court cases involving violations of car rental agreements. According to research, there are a number of ways to resolve payment defaults, including court cases, arbitration, and mediation. The most popular remedies include the time and money needed for the settlement resolution process, as well as the complexity of the law.

Credit Risk Management and Market Performance of Quoted Deposit Money Banks in Nigeria

This study investigated the effect of credit risk management on market performance of deposit money banks in Nigeria. Market performance was used as dependent variable proxy as net assets per share while management quality ratio, capital adequacy ratio, sensitivity to market ratio, credit to deposit ratio and asset quality ratio were used as independent variables. A sample of 12 deposit money banks were used for the period of ten years spanning 2012 to 2021. The study employed ex-post facto and longitudinal research design. The secondary sources of data were collected from annual reports of the selected deposit money banks and five (5) specific objectives and hypotheses were subjected to some preliminary data tests like descriptive statistics, Pearson correlation analysis and Variance Inflation factor (VIF) were analyzed using panel regression analysis. Using a sample of 120 banks-year observations, the result revealed that management quality ratio, credit to deposit ratio and asset quality ratio have negative and statistically significant effect on market performance proxy as net assets per share of deposit money banks in Nigeria which was statistically significant at 5% level of significance. Conversely, Capital adequacy ratio and sensitivity to market ratio was found to have positive but non-significant effect on market performance of deposit money banks in Nigeria. Based on the findings above, the study recommends among others, that managers of Banks in Nigeria should enhance their management quality and capacity in credit analysis to reduce the risk of default in repayment as this will stem the cyclical nature of net assets per share and increase their market performance. Moreover, adequate provision against loan loss should be made and Nigeria banks should adopt an aggressive deposit mobilization to increase credit availability and develop a reliable credit risk management strategy with adequate punishment for loan payment defaults. The st

An ensemble learning approach for anomaly detection in credit card data with imbalanced and overlapped classes

Electronic payment methods have become increasingly popular for business transactions, both online and in-person, across the globe. Anomalies like online fraud and default payments, which can result in substantial financial losses, have become more common as the usage of credit cards in online purchases has increased. To address this issue, researchers have explored various machine learning models and their ensemble techniques for detecting anomalies in credit card transaction data. However, detecting anomalies in this data can be challenging due to overlapping class samples and an imbalanced class distribution. Therefore, the detection rate of anomalies from minority class samples is relatively low, and general learning algorithms can be biased towards the majority class samples. In this paper, we propose a model called Credit Card Anomaly Detection (CCAD) that leverages the base learners paradigm and meta-learning ensemble techniques to improve the detection rate of credit card anomalies. We utilize four outlier detection algorithms as base learners and XGBoost algorithm as meta learner in the proposed stacked ensemble approach to detect anomaly in credit card transactions. We apply stratified sampling technique and k-fold cross-validation process to address the issues of data imbalance and overfitting. In addition, the discordance rate is calculated to enhance the accuracy of ensemble learning performances. The proposed model is trained and tested using two datasets: CCF (Credit Card Fraud) and CCDP (Credit Card Default Payment). Experimental results demonstrate that our approach outperforms existing approaches, particularly in detecting anomalies from the minority class instances of these datasets.

Charaterizing Payment Default of Low-Cost Housing Loans

This study aimed to develop a framework on factors affecting payment default in low-cost housing loans. A total of 462 low-cost housing loan borrowers have participated in the study. Adapted-modified research questionnaire and stratified random sampling technique were employed to gather data from participants. The Exploratory Factor Analysis, Frequency Distribution and Binary Logistic Regression analysis were utilized as statistical tools to analyze the data. It revealed that there were five factors that characterized payment default of low-cost housing loans namely, Loan Effects and Liquidity, Loan Monitoring, Quality or Level of Account Management, Saving Attitude, and Loan Sufficiency/Adequacy. Further, 60 percent of the borrowers are paying their loans on time, meanwhile, 40 percent were paying their loan or before the due date. Using binary logistic regression, the study concluded that loan effects and liquidity (0.005 < 0.05), and saving attitude (0.000 < 0.05), were found to significantly influence the payment default of borrowers.

The Awareness Of CIPAA in Remedying Payment Issue Among Contractors In Malaysia

Payment in the Malaysian construction industry has generally been an issue of concern. The government has embarked on severe afford to reduce payment default issues by enacting the Construction Industry Payment and Adjudication Act (CIPAA) in 2014. Since CIPAA was relatively new, the possibility of the industry’s key players needing more information and awareness about CIPAA is greater. Thus, this research aims to investigate the current level of awareness of CIPAA and identify the strategies to increase the awareness level of CIPAA among contractors in Malaysia. The research methods used for this study are quantitative. The data are collected from previous studies, journals, articles, questionnaire surveys, and interviews. The result shows that many contractors in Malaysia still need to be made aware of CIPAA. The level of awareness of the contractors in Malaysia towards CIPAA is still low. For future recommendations, the contractor in Malaysia must be provided with more guidelines and awareness regarding the CIPAA.

효과적 전자대금지급시스템의 영향요인에 관한 사례분석적 연구

Our society has long grappled with numerous issues such as wage and payment defaults in construction projects due to the lack of transparency. In response, Seoul Metropolitan Government has built and implemented a electronic payment system, known as the “Daegum-e-Baro” system, as part of its e-Government innovation initiatives. This electronic payment system has been expanded nationwide and is now legally mandatory for all public construction projects. Consequently, it is imperative for this study to examine the potential effects and influencing factors of the electronic payment system in the context of policy evaluation. Previously, studies on construction corruption have typically focused on cases of illegal subcontracting improvements. However, in this research, the study approaches from the perspective of the principal-agent theory, concentrating on the issues of information asymmetry and agent problems. This study aims to analyze the expected effects and influencing factors of the Daegum-e-Baro system. Based on enhancing transparency through e-Government, this study comprehensively examined what execution strategies and influencing factors were at play during the implementation process of the Seoul electronic payment system policy, how it expanded to national policy, and what the current outcomes look like. As expected effects, this study deduced control of corruption and fairness. As influencing factors, this study presented technical factor, legal and institutional factor, managerial factor, and transparency factor. Lastly, this study proposed policy suggestions for the development of the electronic payment system and added research implications and limitations.

Risk-Taking Behavior of Adolescents and Young Adults Born Preterm.

To study sexually transmitted Chlamydia trachomatis infections (STCTs), teenage pregnancies, and payment defaults in individuals born preterm as proxies for engaging in risk-taking behavior. Our population-based register-linkage study included all 191 705 children alive at 10years (8492 preterm [4.4%]) born without malformations in Finland between January 1987 and September 1990 as each mother's first child within the cohort. They were followed until young adulthood. We used Cox regression to assess the hazards of STCTs, teenage pregnancies, payment defaults, criminal offending, and substance abuse by gestational age. Gestational age was considered both as a continuous and categorical (extremely, very, moderately, late preterm, early term, post term, and full term as reference) exposure. A linear dose-response relationship existed between gestational age and STCT and teenage pregnancy; adjusted hazard for STCT decreased by 1.6% (95% CI, 0.7%-2.6%), and for teenage pregnancy by 3.3% (95% CI, 1.9%-4.8%) per each week decrease in gestational age. Those born extremely preterm (23-27 completed weeks) had a 51% (95% CI, 31%-83%) lower risk for criminal offending than their full-term born counterparts, and those born very preterm (range, 28-31weeks) had a 28% (95% CI, 7%-53%) higher hazard for payment defaults than those born at full term. Gestational age was not associated with substance abuse. The lower risk-taking that characterizes people born preterm seems to generalize to sexual and to some extent criminal behavior. Those born very preterm are, however, more likely to experience payment defaults.

RELIABILITY OF CREDIT SCORES IN LOAN MANAGEMENT IN A DEVELOPING COUNTRY

In developing countries, loan sales for expensive products are very common but highly risky because of frequent payment defaults. Because personal credit histories in these countries are imprecise and insufficient compared with those in developed countries, the use of credit scores in loan sales may be very unreliable. Here, to evaluate the reliability of credit scores, we build a simple cost-benefit model of loan management, dividing credit scores into 10 credit score classes (CSCs). As CSC increases, the rough net benefits increase because the total losses caused by payment defaults decrease. The use of credit scores in loan sales is thus found to be highly reliable in developing countries. Combining credit scores with newly developed sources (e.g., mobile phones) may provide highly accurate estimates for loan management.

Influencing Factors of Property Use Conversion and Residential Properties Rental Value Trend in Ikeja, Nigeria

The paper determines the influence of property use conversions on residential property rental value performances in Ikeja, Nigeria. Previous studies have shown that a multiplicity of use conversion characterizes Nigeria urban centres, hence, threatening the cordial relationship among the property market participants, rental value performances, distorting city aesthetic and morphology. The rapidity of this trend is alarming, thus call for the urgent attention of land/city managers, spatial planner, government and other stakeholders to address its attendant problems. Quantitative data, using questionnaire survey, were collected and used for the study. The influence of property use conversion factors on residential property rental value performances in the study area was determined using a predictive model. Findings of the study show that about 67% of use conversion activities in Ikeja can be explained through the fusions of the conversion factors in the predictive model with 96% influence on residential property rental values. It was further observed that there exist increasing cases of default in rent payment due to the conversion activities and intensive competition for property uses especially properties with locational advantages. The paper advocates for strict implementation of all relevant sections of urban property use related laws in the state to ensure compliance with existing laws and appropriate monitoring of property development and uses that will help in the achievement of sustainable city development and liveability in the post pandemic era.

Central Counterparty Default Waterfalls and Systemic Loss

AbstractCentral counterparty (CCP) default waterfalls act as the last lines of defense in over-the-counter markets by managing and allocating resources to cover payment defaults. This article examines the impact of variations in waterfall design on financial system losses in the presence of payment network dependencies and frictions in the cleared and noncleared portion of the system. Through the development of a structural model, we draw several theoretical conclusions about the effectiveness of CCP default waterfalls under severe payment stress. These findings are empirically quantified by testing the model using supervisory data for the U.S. credit default swap market.