Payment Default Research Articles

Financial default payment predictions using a hybrid of simulated annealing heuristics and extreme gradient boosting machines

Online peer-to-peer (P2P) lending platforms face multiple challenges in today's e-commerce, but one of the most outstanding concerns evaluating loan risk based on borrowers' financial status and histories. Traditionally, financial experts assess borrowers' risk of default payments manually, but this process is tedious and time consuming, which are not widely applicable concerns for online P2P platforms. This paper proposes a hybrid of the simulated annealing and the extreme gradient boosting machine models in order to predict the likelihood of default payments based on users' finance histories. Based on the experimental results, the proposed model demonstrates its predictability with high recall scores. The proposed model not only out-performs over conventional algorithms including logistic regressions, support vector machines, random forests, and artificial neural networks, but it also provides an efficient method for optimising hyper-parameters in the machine learning algorithms. Through the utilisation of the proposed data-driven models, the necessity of tedious and potentially inaccurate human labour can be significantly reduced, and service level agreements (SLAs) can be further improved by time reduction made possible through the introduction of advanced data mining approaches.

WANPRESTASI PEMBAYARAN KLAIM ASURANSI JIWA AKIBAT KELALAIAN PENYERAHAN BERKAS OLEH MITRA PENANGGUNG SEBAGAI KOLEKTOR PENGAJUAN KLAIM (Studi Kasus Sertifikat Asuransi Polis Nomor 15.001673)


 
 
 At this time many banks are incentive to lure consumer credit to consumers. In general, consumer loan interest rate is higher than productive credit, even there is a fixed rate. It seems that the fixed rate makes it easy to organize family finances, paying only monthly installments of the same amount, but if carefully calculated, the interest is much higher. Consumer Loan Protection with Insurance Policy Certificate 15.001673 is a life insurance product that guarantees repayment of the remaining amount of Loans and / or monthly loan installment of the Customer as Participant (Insured) to the Policyholder in case the Participant (the Insured) has a death risk or total temporary disability / Or total permanent disability. Although it is clear about the rights and obligations in the insurance agreement but the reality is very different because it turns out the insurer does not fulfill its obligations in the event of claim submission from the insured. Rejection of insurance claims may be made by the insurer under the pretext of submitting the file beyond the specified time limit. Issues to be studied further is how validation of denial of life insurance claim made by Jasindo in accordance with the insurance policy and existing legislation and whether Partner Error is can be classified as Wanprestasi payment of Insurance Claim for late in submission of policy file No: 15.001673. In conducting research, this research is normative law research that is research having object of study about rule or rule. The objective is to determine the validity of the refusal of insurance claims made by Jasindo in accordance with the existing insurance policies and regulations and to find out the Default Payment of Insurance Claims due to Delayed Submission by Marketing Party as Collector Submission of Claim on Insurance Certificate Number 15.001673
 
 

Analysing Punishment of Corporations in Water Pollution Case

For a stiffer penalty, punishment for the environmental violation was generally increased from Malaysian Ringgit 10,000 to Malaysian Ringgit 100,000 and from two years to five years imprisonment twenty years ago. Although the penalties are improved, the cases of environmental pollution and violation are yet increasing. This article examines the trend of punishment for environmental violations that imposed on the corporate entities or corporations. Focusing on the case of water pollution, the observations are made to the penalty’s provisions of the Environmental Quality Act 1974 and the penalties imposed through the court judgment against the corporations. For this purpose, cases and charges of water pollution are obtained from the records of Department of Environment Malaysia. Moreover, views of the judges of the Green Court on both laws and judgments relating to penalties are acquired. The study found that: (a) there is a wide gap between the punishment imposed by law and the penalties positioned by court; (b) corporate entities are willing to pay fines; (c) courts are not ready to impose imprisonment for default of payment to the corporations or individuals within the corporations. Though addressing water pollution, the findings should be far-reaching surpassing other types of environmental pollution.

Is Peru Prepared for Large-Scale Sustainable Rural Electrification?

Peru has historically been among the Latin-American countries with a low rural electrification rate. Aiming to improve this situation, the country conducted several electrification efforts in the last few decades that included off-grid photovoltaic (PV) solutions for remote areas (where the grid expansion was unviable). More recently, the government has also sponsored a ‘massive program’ that aims to deploy a minimum of 150,000 off-grid PV solutions in the upcoming years. In this paper, we assess the sustainability of rural electrification programs in Peru, paying special attention to the ongoing “massive program”. Our assessment considers four dimensions of sustainability (institutional, economic, environmental, and socio-cultural) and is based on an exhaustive qualitative document analysis complemented by semi-structured expert interviews. We found that the lack of strong formal institutions with a flexible and decentralized structure seriously compromises the sustainability of rural electrification efforts in Peru. Staff rotation and overlapping competences have caused disturbing changes and inhibited following a strategic line, while widespread outsourcing combined with weak controls have often affected the reliability of the deployed systems. Although cross subsidies have made off-grid PV systems affordable for users, systems often fell short of energy demand. Notably, we found that Peruvian officials appear to be unaware of the importance of local participation, and there is a significant mistrust between the government and the rural population (especially in areas where mining is extensive). As a consequence, most of the projects are still designed without the participation and engagement of the communities, which has frequently led to project failures, payment defaults, and inhibited seizing opportunities regarding productive uses of off-grid PV systems. We expect that our findings may help Peruvian institutions to address the most severe drawbacks affecting their rural electrification efforts based on off-grid PV systems.

An artificial intelligence system for predicting customer default in e-commerce

The growing number of e-commerce orders is leading to increased risk management to prevent default in payment. Default in payment is the failure of a customer to settle a bill within 90 days upon receipt. Frequently, credit scoring (CS) is employed to identify customers’ default probability. CS has been widely studied, and many computational methods have been proposed. The primary aim of this work is to develop a CS model to replace the pre-risk check of the e-commerce risk management system Risk Solution Services (RSS), which is currently one of the most used systems to estimate customers’ default probability. The pre-risk check uses data from the order process and includes exclusion rules and a generic CS model. The new model is supposed to replace the whole pre-risk check and has to work both in isolation and in integration with the RSS main risk check. An application of genetic programming (GP) to CS is presented in this paper. The model was developed on a real-world dataset provided by a well-known German financial solutions company. The dataset contains order requests processed by RSS. The results show that GP outperforms the generic CS model of the pre-risk check in both classification accuracy and profit. GP achieved competitive classificatory accuracy with several state-of-the-art machine learning methods, such as logistic regression, support vector machines and boosted trees. Furthermore, the GP model can be used in combination with the RSS main risk check to create a model with even higher discriminatory power.

Supply chain finance risk management

Based on mechanism design theory on asymmetric information, this study devises a selectable menu of contracts with price discounts and trade credit rates and proposes strategies for mitigating the risk of payment default when a tourism service provider trades with unfamiliar tour operators. Tourism service providers are suggested to remain conservative under a positive economic condition and progressive under a moderate economic condition when engaging in a new business opportunity. However, a fringe level of progressivism may be maintained under a moderate economy and a low-risk environment. The net benefit of tourism service providers increases when the bank rate is high, and the profit of the entire tourism channel increases when the percentage or ability to pay reliable agents is high.

Ensemble Learning or Deep Learning? Application to Default Risk Analysis

Proper credit-risk management is essential for lending institutions, as substantial losses can be incurred when borrowers default. Consequently, statistical methods that can measure and analyze credit risk objectively are becoming increasingly important. This study analyzes default payment data and compares the prediction accuracy and classification ability of three ensemble-learning methods—specifically, bagging, random forest, and boosting—with those of various neural-network methods, each of which has a different activation function. The results obtained indicate that the classification ability of boosting is superior to other machine-learning methods including neural networks. It is also found that the performance of neural-network models depends on the choice of activation function, the number of middle layers, and the inclusion of dropout.

Venezuela's Imminent Restructuring and the Role Alter Ego Claims May Play in this Chavismo Saga

The clock ticking down for investors holding the outstanding debt of the Republic of Venezuela and its state-owned oil company, Petroleos de Venezuela, S.A. (“PDVSA”), may have just struck zero. On Friday, November 3, President Nicolas Maduro kicked off the much anticipated restructuring of Venezuelan debt by announcing that after it makes a $1.1 billion principal payment on PDVSA bonds due on November 2, that it would commence restructuring negotiations with its creditors. Although the Government invited creditors to Caracas on November 13 to jump start negotiations, given the failed policies of the Maduro regime, the limitations posed by U.S. government sanctions and the risks creditors would face in accepting new instruments that could be challenged by a future Venezuelan government, the prospects of any type of restructuring being accomplished anytime soon are quite remote. Should Venezuela fail to cure its existing payment defaults or not make payments during the pendency of any restructuring discussions, which seems to be the government’s intent, one can expect Venezuela’s legion of creditors to turn their immediate attention to scouring the globe for assets held in the name of the Republic and those entities, such as PDVSA, alleged to be the “alter egos” of the Republic. This article discusses the legal framework for pursuing alter ego claims, including the continued efforts by Republic creditor Crystallex International Corporation (“Crystallex”), a Canadian gold-mining corporation, to collect on its $1.4 billion U.S. court judgment against the Republic from the assets of PDVSA, and evaluates the ability of other Republic creditors to pursue a similar strategy. One thing is clear: Crystallex’s efforts to pursue its alter ego claims against PDVSA will be closely watched by Republic and PDVSA creditors alike.

Rural Electrification Efforts Based on Off-Grid Photovoltaic Systems in the Andean Region: Comparative Assessment of Their Sustainability

In this paper, we comparatively assess the sustainability of rural electrification efforts based on off-grid solutions in Chile, Ecuador, and Peru. Our assessment considers four dimensions of sustainability (institutional, economic, environmental, and socio-cultural). We found that Ecuador and Chile have consistently failed to ensure mechanisms for the operation and maintenance of the deployed off-grid systems, which has made these solutions in poor Chilean and Ecuadorian communities inevitably unsustainable. Although Peru has adopted a cross-tariff scheme, the Peruvian case shows that ensuring the funding of off-grid PV solutions is not enough. Peruvian officials appear to be unaware of the importance of local participation (local values and lifestyles are constantly disregarded) and most of the projects have been designed without the participation and engagement of the communities, which has often led to project failures and payment defaults. However, although each country has its particular challenges, we found that the three Andean countries have consistently neglected the importance of strong formal institutions with a flexible and decentralized structure, which in turn significantly compromised the rural electrification effort in these countries.

Sovereign Debt and Moral Hazard: The Role of Collective Action and Contractual Ambiguity

The ambiguous phrasing of pari passu (equal treatment) clauses in sovereign debt contracts has long baffled commentators. We show that in the presence of asymmetric information on a sovereign borrower's ability to pay, an uncertain clause gives rise to a collective action problem among creditors that can reduce sovereign moral hazard. By varying the clause, parties can calibrate a sovereign's expected default costs and payments to creditors and thereby optimally trade off the sovereign's moral hazard and (deadweight) default costs. As information asymmetry decreases, a pari passu clause becomes a coarser instrument for configuring creditors' incentives and mitigating moral hazard.

Predicting financial distress of agriculture companies in EU

The objective of this paper is the prediction of financial distress (default of payment or insolvency) of 250 agriculture business companies in the EU from which 62 companies defaulted in 2014 with respect to lag of the used attributes. From many types of classification models, there was chosen the Logistic regression, the Support vector machines method with the RBF ANOVA kernel, the Decision Trees and the Adaptive Boosting based on the decision trees to acquire the best results. From the results, it is obvious that with the increasing distance to the bankruptcy, there decreases the average accuracy of the financial distress prediction and there is a greater difference between the active and distressed companies in terms of liquidity, rentability and debt ratios. The Decision trees and Adaptive Boosting offer a better accuracy for the distress prediction than the SVM and logit methods, what is comparable to the previous studies. From the total of 15 accounting variables, there were constructed classification trees by the Decision Trees with the inner feature selection method for the better visualization, what reduces the full data set only to 1 or 2 attributes: ROA and Long-term Debt to Total Assets Ratio in 2011, ROA and Current Ratio in 2012, ROA in 2013 for the discrimination of the distressed companies.

Tax payment default prediction using genetic algorithm-based variable selection

Abstract According to the statistics from the Finnish tax authorities, about 12% of all active firms in Finland had unpaid taxes at the end of year 2015. In monetary terms, this translates to over 3 billion euros in unpaid taxes. This is a highly significant amount as the total amount of taxes collected during 2015 was 49 billion euros. Considering the economic significance of the unpaid taxes, relatively little research has been done on identifying tax defaulting firms. The objective of this study is to develop a genetic algorithm-based decision support tool for predicting tax payment defaults. More closely, a genetic algorithm is used for determining an optimal or near optimal subset of variables for a linear discriminant analysis (LDA) model that classifies the examined firms as either defaulting or non-defaulting. The tool also provides information about the importance of various variables in predicting a tax default. The dataset consists of Finnish limited liability firms that have defaulted on employer contribution taxes or on value added taxes and the total number of available variables is 72. The results show that variables measuring solvency, liquidity and payment period of trade payables are important variables in predicting tax defaults. The best performing model comprises three non-linearly transformed variables and has a predictive accuracy of 73.8%.

ENFORCEMENT OF SECURITY INTEREST UNDER SARFESI ACT, 2002

When a Secured Creditor issues a secured loan to a borrower then the borrower as per the terms of the mortgage contract between them is expected to pay the money back and when the borrower makes default in payment of the secured debts or installments thereof then the Bank may take possession of the property secured and get back its money by classifying the secured asset as Non Performing Asset and then following all the procedural requirements of Chapter III (Section 13-section 16) of the SARFESI Act 2002 and The Security Interest Enforcement Rules, 2002. Thus, through chapter III of the SARFESI Act 2002 legislatures wanted to sort out the conundrum which Debt Recovery Tribunal as constituted under The Recovery Of Debts Due To Banks And Financial Institutions Act, 1993 (hereinafter called RDDBFI Act) had caused to the bank by its manifest inability to execute its order of recovery of debt. Thus, with chapter III of the SATFESI Act 2002 Parliament has once again tried to rejuvenate the tenuous economy throttled by backlog of cases and so as to revive faith of the investors in Indian market.

Wealth and Imprisonment: A Critical Analysis on Inmates Under the Imprisonment Terms for Default Payment of Fine in Sri Lanka with Special Reference to Kegalle and Kuruwita Prisons

This research mainly focused on the inmates under imprisonment terms for default payments of fine. This study aims to find the rationale behind the default terms and how these terms are affected to the non -wealth inmates. Generally, this research based on direct interviews with defaulters under the imprisonment terms. Prison officials and a limited sample of lawyers were directly interviewed and collected their personnel experience and suggestions with regard to defaulters. The literature review was employed throughout the research to collect a wide spread knowledge in relation to defaulter population and this review allowed to collect the various resources in to one arena. This work identified that there are plenty of reasons for the imprisonment for default payment of fine besides of low income level of the offenders. The research focuses the domestic laws and international laws relating to imprisonment for default payment of fine. The purpose of this research is gather qualitative information in relation to those prisoners who are inability to pay their fine. This qualitative information will help in the formulation of recommendation when default terms are imposed, by identifying the measures most likely to be of assistance in ensuring payment of the fines and the measures which might prevent the imprisonment of persons for non-payment of fines due to poor income level.

Gerenciamento do Ponto de Corte na Concessão do Crédito Direto ao Consumidor

Resumo O crédito direto ao consumidor é concedido de forma massificada por meio de modelos informatizados de credit scoring que visam eliminar os maus pagadores, assim evitando prejuízos financeiros decorrentes da inadimplência. No entanto, ao recusarem os maus pagadores, esses modelos, balanceados por critérios meramente financeiros, também recusam um grande número de bons pagadores, o que pode reduzir o potencial de ganhos da empresa varejista. Esta pesquisa teve por objetivo mostrar que, incluindo a margem operacional para definir o ponto de corte para concessão de crédito, a rentabilidade da empresa pode melhorar. Foram utilizadas a medida estatística Receiver Operating Characteristic (ROC) e a abordagem de Stein (2005) para construir simulações em torno de valores reais praticados no mercado, as quais permitiram confirmar que, ao flexibilizar o ponto de corte levando em consideração a margem operacional, o resultado geral da empresa pode ser maximizado.

Bayesian Network Modeling: A Case Study of Credit Scoring Analysis of Consumer Loans Default Payment

This paper deals with the issue of predicting customers’ default payment. The Bayesian network credit model is applied for the prediction and classification of personal loans with regard to credit worthiness. Referring to credit experts and using K2 algorithm for learning structure, we set up the dependency conditional relations between variables that explain default payments. Then, the parametric learning is adopted to detect conditional probabilities of customers’ default payment. The parameters are estimated on the basis of real personal loan data obtained from a Tunisian commercial bank. The Bayesian network analysis has revealed that customers’ age, gender, type of credit, professional status, and monthly repayment burden and credit duration have an important predictive power for the detection of customers’ default payment. Therefore, our findings allow providing an effective decision support system for banks in order to detect and reduce the rate of bad borrowers through the use of a Bayesian Network model.

企业信用赊销成本与组合信用赊销研究

随着我国市场经济和信用经济的快速发展，市场主体之间的信用交易越来越普遍。生产企业为了使产品快速进入市场，赢得时间和规模效应，信用赊销(Credit Selling)便成为常见的销售模式。当前，我国企业间账款拖欠现象十分严重，其根本原因是企业内部的信用管理机制不健全，缺少有效的信用赊销风险管理体系。本文针对信用赊销风险管理的关键环节——信用赊销的成本以及组合赊销展开讨论，通过对信用赊销的成本分析，建立了最优应收账款持有量与最低信用赊销成本之间的关系模型，并提出组合信用赊销的优化方案，使企业信用赊销的收益与其承受的风险相匹配。 With the rapid development of our market economy and credit economy, the credit transactions between market players are becoming more and more common. Credit Selling has become a common sales model for enterprises to gain time and scale. At present, payment default phenomenon between our country enterprise is very serious, the fundamental reason is the enterprise internal credit management mechanism is not sound, lack of effective Credit Selling risk management system. Aiming at the key link of Credit Selling risk management, the cost of Credit Selling and credit portfolio were discussed, through the analysis of the cost of Credit Selling established the optimal receivables holdings and the relationship between the minimum Credit Selling cost models, and put forward the combination, which matches the risk of the company’s earnings on credit.

Evaluation of Risk Elements In Real Estate investment In Nigeria: The Case Of Uyo Metropolis, Akwa Ibom State

Risk is a common feature of all forms of investment including real estate which involves the creation of new income yielding assets from land and its resources based on capital analysis of expected costs and benefits within a given time. While some real estate investments have a high-risk profile, others have low-risk profile depending on the type, location and lease term. This study was designed to evaluate the different elements of risk in relation to different stages of real estate development in Uyo Metropolis, Akwa Ibom State. A total of 200 project sites were randomly selected and visited for data collection. Accordingly, 200 sets of questionnaire containing items bordering on types of risk/ project development were administered to the private estate developers while simple percentages was employed to analyse data. Findings indicated that the nature of risk encountered by investors varied according to the stages of project development, especially during preparation of development plans and building construction. Majority of respondents identified lack of access to project finance, delay in issuance of C-of-O and default in rent payment as the major risk elements in their investment. Based on these findings, it was recommended that real estate investors and managers should evolve effective and efficient management strategies to mitigate dominant risk elements for enhanced investment opportunity in the real sector.

Mortgage debt and household vulnerability

PurposeThis paper aims to study the financial vulnerability of the Spanish households derived from their primary residence mortgage debt payments. This paper shown as the economic and financial crisis triggered after the burst of the housing bubble brought an unemployment shock and a fall in the disposable family income, which alarmingly aggravated the financial vulnerability of the mortgaged households. Consequently, the number of financially vulnerable households almost doubled.Design/methodology/approachEconometric model of discrete election.FindingsThe most vulnerable households – and therefore those with a higher risk of mortgage payment default – are those whose family head is a married and self-employed female. In contrast, in social housing the mortgaged households have been less vulnerable in the context of economic and financial crisis and unlike what would have been initially expected, higher education levels have not acted as a protective factor against households’ financial vulnerability.Originality/valueThere is a great need to understand how the financial health of the mortgaged families that bought their primary residence has deteriorated in a context of significant changes in macroeconomic conditions. This need is specially pressing in a country such as Spain which is one of the OECD’s countries with a higher rate of household property and which shows a sector of highly mortgaged households.

Indicators of financial distress - An empirical study of Indian Textile sector

 AbstractBusinesses across the globe faces challenges to ensure stability, growth and sustainability. Companies have to deal with changes in economic, social, cultural, political and technological environment. Companies failing to do may face financial distress causing default in payment of contractual obligations and erosion of shareholders wealth. In a business scenario where the stakeholders are many viz. shareholders, lenders, employees, government and society at large, protection of the interests of the stakeholders assume prime importance. Company’s management are expected to identify signals that indicate distress and take remedial measures. This paper attempts to identify distress signals in textile sector in India. Textile sector is one of the largest sector in India. However one third of companies in this sector have reported losses for the previous year. This study aims to examine the factors that can differentiate a distressed company from a non- distressed company so that the factors signifying distress can be studied. Listed companies in textile sector incurring continuous losses for three years were selected for the study. Financial ratios were used as variables. Logistic regression was applied to identify the most important factors indicating distress. It was observed that ratios measuring profitability and efficiency were significant in predicting distress. Key words:  Financial distress, distress signals, textile sector, continuous losses, financial ratios