Default Rates Research Articles

Can Social Notifications Help to Mitigate Payment Delinquency in Online Peer‐to‐Peer Lending?

Payment management is an operations management challenge in online peer‐to‐peer (P2P) lending. It is critical to control the cost of debt collection incurred in late repayments and defaults. We study whether and how a platform can leverage on borrowers’ social connections and use automatic social notifications in regulating repayment behavior. In collaboration with a large online P2P lending platform, we conduct a randomized field experiment to investigate the effect of social notifications targeted at different contact groups (core‐circle and peripheral‐circle groups). Our results indicate that notifying social contacts of a delinquent regarding the overdue payment significantly improves the repayment rate. Compared with the control group in which no notification messages are sent to social contacts, notification‐triggered social sanctions and social support reduce the default rate by more than 50% in both core‐circle and peripheral‐circle groups. Furthermore, we find that social notifications targeted at peripheral‐circle social contacts are only effective in the short term, and its effectiveness decreases with repeated use. By contrast, social notifications targeted at core‐circle social contacts have a lasting effect.

Corporate stress and bank nonperforming loans: Evidence from Pakistan

<ns2:p>Using detailed administrative Pakistani credit registry data, we show that banks with low leverage ratios are both significantly slower and less likely to recognize a loan as nonperforming than other banks that lend to the same firm. Moreover, we find suggestive evidence that this lack of recognition impedes loan curing, with banks with low leverage ratios reporting significantly higher final default rates than other banks for the same borrower (even after controlling for differences in loan terms). Our empirical findings are consistent with the theoretical prediction that classifying a nonperforming loan is more expensive for banks with less capital.</ns2:p>

Do investors prefer borrowers from high level of trust cities? Evidence from China’s P2P market

This study explores the effects of trust on individuals’ access to the peer-to-peer (P2P) lending market. We use data collected from the P2P lending market and the China General Social Survey and find that borrowers from cities with high trust have high borrowing success rates, thereby indicating that lenders prefer high social trust. Results in the successful sample indicate that borrowers with high regional trust also receive low loan costs and large loan amounts. Regression of trust and default proves that borrowers from cities with high social trust have minimal default rates, which may be the channel of our conclusion. Results remain unchanged when using slope and river length as instrumental variables. This research further shows that personal heterogeneity, including income level, whether borrowers work in state-owned enterprises or state agencies, and whether the fund is used for development purposes, affects the impact of social trust. In addition, the conclusions continue to be robust after replacing the explanatory variable, control variable, and sample. Finally, this study determines that fairness plays a consistent role with trust, but happiness plays an opposite role.

The use of migration matrices to test the sustainability of the top ranked restaurants: a methodological note

PurposeThe purpose of this paper is to present a new methodology to estimate the migration of grades of top rated restaurant and the default rate over time.Design/methodology/approachTo demonstrate how to develop migration matrices the empirical results are based on the real number of top-rated French restaurants in Gault-Millau in a specific year and how they migrate from one grade to another over the period 1974–2010.FindingsThe purpose of the empirical analysis is only to illustrate the methodology. It is shown that migration rates are relatively stable over time.Research limitations/implicationsResults are presented only to illustrate the methodology. Further analysis could provide a sound basis to compare the rating systems from one guide to another.Originality/valueThis research note explores the notion of migration rate by developing an alternative way of measuring how restaurants survive over time.

Promises and pitfalls of digital credit: Empirical evidence from Kenya.

Digital credit is a recent innovation that raises hopes of improving credit access in developing countries. However, up until now, empirical research on the extent to which digital credit actually reaches people who are otherwise excluded from conventional credit markets and whether increased credit access is sustainable or threatened by high default and blacklisting rates is very scarce. Using representative data from Kenya, this article shows that digital credit increases borrowing opportunities, including for people less likely to otherwise have credit access in the conventional credit markets. However, we find that digital credit borrowing is also responsible for 90% of all blacklistings, which is partially driven by higher default rates in the digital credit market but also by a higher probability that digital credit defaults lead to blacklisting of the borrower, compared to defaults in other credit markets.

Impact of income diversification strategy on credit risk and market risk among microfinance institutions

PurposeThis study explores the effect of income diversification strategy on credit risk and market risk of microfinance institutions (MFIs) in Ghana as an emerging market.Design/methodology/approachThe study is based on quarterly data of averagely 271 MFIs that have operated from 2016 to 2018. The dataset is unbalanced and pooled cross-sectional with 3,259 data points. The study measures the diversification strategy using income diversification indices, and accounting ratios to measure the other variables. We utilised the weighted least squares (WLS) approach to explore the nexus.FindingsThe findings show that income diversification is associated with better loan quality and credit risk management. Market risk increases with the level of income diversification of microfinance firms. It is evident that large MFIs can manage their credit risks well and can have a low default rate, depicting an overall U-shaped nexus. On the other hand, the effect of size on market risk is an inverted U-shaped. The effect of asset tangibility on credit risk is positively significant while the effect on market risk is negatively significant. High profitability enhances credit risk management leading to lower loan losses while in the case of diversified and profitable MFIs, they tend to invest more in government securities. The results suggest that MFIs that hold more cash and cash equivalents tend to have high loan loss provision and more government securities suggesting much attention should be paid to optimal cash management.Practical implicationsThe results throw light on the credit risk and market risk profile of the firms and the effect of diversification strategies on them. The findings are relevant for effective macroprudential regulation, market regulation and prudential regulation of the microfinance sector.Social implicationsThe findings reveal the nature of income diversification strategy of MFIs in emerging markets such as Ghana, pointing out how they affect the risk exposure of MFIs that lend to the pro-poor population.Originality/valueThis is a premier formal assessment of the nexus between income diversification strategies and risk management among MFIs that serve the pro-poor population in the emerging market context.

Calibrating the CreditRisk+ Model at Different Time Scales and in Presence of Temporal Autocorrelation †

The CreditRisk+ model is one of the industry standards for the valuation of default risk in credit loans portfolios. The calibration of CreditRisk+ requires, inter alia, the specification of the parameters describing the structure of dependence among default events. This work addresses the calibration of these parameters. In particular, we study the dependence of the calibration procedure on the sampling period of the default rate time series, that might be different from the time horizon onto which the model is used for forecasting, as it is often the case in real life applications. The case of autocorrelated time series and the role of the statistical error as a function of the time series period are also discussed. The findings of the proposed calibration technique are illustrated with the support of an application to real data.

Loan Delinquency: Some Determining Factors

The main objective of this paper is to investigate the determining factors of loan delinquencies from the perspective of borrower attributes and loan characteristics. Empirical results indicated that the borrower-lender distance factor, collateral, education levels as well as availability of a monthly budget are having significant effects on loan delinquencies. On the other hand, level of income and gender have no significant impact on repayment behaviour. Credit is good as it allows the borrowers financial flexibility, however, debt is viewed as bad if it was not managed properly. Therefore, a correct attitude towards credit management and self-discipline can be encouraged to reduce loan default rates.

Predicting corporate defaults using machine learning with geometric-lag variables

ABSTRACT This study examines whether corporate default prediction techniques based on machine learning can achieve better performance by using geometrically declining weighted average values of the time series variables, that is, geometric-lag variables. We test four machine learning algorithms: logistic regression, random forest, support vector machine, and feedforward neural network. The geometric-lag financial variables capture each company’s historical financial information. Using such variables reduces the computation time and improves the prediction performance. The actual default rates increase with the predicted default probabilities, suggesting that our model predictions can help investors make better investment decisions.

Mortality Rate and Ten Years Survival of Elderly Patients Treated with Total Hip Arthroplasty for Femoral Neck Fractures.

Introduction:Mortality following traumatic femoral neck fractures in the elderly (age >60 years) is influenced by many factors. Addressing some of them may reduce the mortality rate thus improving patient survival and quality of life.Materials and methods:This study was a retrospective research using data collected from Hospital Sultanah Bahiyah, Kedah between the years 2008-2018. We measured outcomes such as age, gender, hospital stay, default rate, ambulation post-surgery, American Society of Anaesthesiologists score (ASA) and surgical timing in correlation with mortality rate and 10-year survival of elderly patients treated with Total Hip Arthroplasty for femoral neck fractures in this centre.Results:A total of 291 traumatic femoral neck fractures aged above 60 years post total hip arthroplasty performed were included. There was higher number of female (n =233) compared to male (n=53) Estimated 10 years survival from Kaplan Meier was 42.88% (95% CI: 33.15, 52.54). One year mortality rate in our study was found to be 18.9%. The average time to event was 7.1 years (95% CI:33.15, 52.24) with a mean age group of 75.Discussion:Total hip arthroplasty patients not ambulating after surgery had a 4.2 times higher hazard ratio compared to ambulators. Those with pre-existing systemic disease (ASA III and IV) were found to have the highest hazard ratio, almost five times that of healthy patients, after adjusting for confounding factors. Delay of more than seven days to surgery was found to be a significant factor in 10-year survival with a hazard ratio of 3.8, compared to surgery performed earlier.Conclusion:Delay of more than 7 days to surgery in 10 years survival was significant with high hazard ratio. It is a predictor factor for survival in 10 years. A larger sample size with a prospective design is required to confirm our findings regarding “unacceptable surgical timing” for femoral neck fractures in patients above 60 years of age.

Research on breach prediction for big data through hybrid ensemble learning and logistic regression

The fast-growing online lending sector faces the risk of borrowers defaulting. Network lending data are often unbalanced. On the basis of EasyEnsemble algorithm, we proposed a hybrid ensemble algorithm, which combines the advantages of Bagging and Boosting. By comparing the performance of different learning models, the experimental results show that the hybrid ensemble algorithm has good classification accuracy and generalization ability. Further, we find that social capital has a negative effect on borrower default rate. This paper provides a relatively reliable method for the credit risk assessment of online borrowers, and also provides a new idea for risk monitoring in the field of online lending.

Student Repayment Crisis and the Value of Higher Education and the Economy in California’s Kern County

The cost of post-secondary education (PE) continues to increase, which has contributed to elevating federal loan demand, and as of the fourth quarter of 2020, equaling a debt of $1.56 trillion in the US. The purpose of this research was to compare two post-secondary institutions for specific alignment with the local labor market, examine institutional economic benefits and costs, and impact of loan default. Bakersfield College (BC) and California State University, Bakersfield (CSUB) are both public, Hispanic Serving Institutions, in central California. Despite similarities, loan default rates of each institution differ; six-year mean rates, 24.6% at BC, 7.7% at CSUB. The analysis revealed that although the top degrees at BC and CSUB did not align well with local labor market demands, the individual and institutional economic benefit exceeds the costs. Importantly, both the individual and institutional economic benefits are highly dependent on completing the degree, the time to graduation, and then entering the labor market. The value of this research, specifically a cost-benefit analysis to examine recent trends in local wages, tuition fees, defaults rates, poverty, and alignment with the local labor market, provides insight on the impact of local PE on the individual and the community, providing both educational and economic policy direction.

DETERMINANTS OF EFFECTIVENESS REPAYMENT APPS AT P2P LENDING PLATFORM DURING COVID 19 PANDEMIC IN INDONESIA

Abstract— The advance of technology development today brings an innovation in the financial industries all across the world, including in Indonesia. The financial technology continues to emerge from the sector of payment, investment and loans online or as we are familiar with the term P2P Lending platform. But, the growth of this business had a challenge when the Covid-19 Pandemic hit the country in the early 2020. The P2P lending platform experienced the first highest default rate due the difficulties of the SME’s as its major customers’ in repaying their loans. The purpose of this research is to analyze the determinants of effectiveness repayment apps for ultra-micro segment on P2P platform in Indonesia. As a quantitative research, primary data were collected through questionnaire answered by 133 respondents who were borrower of the P2P lending platform. The data collection was furthered processed statistically using Structural Equation Method (SEM). The study showed that the repayment decision had significantly influenced by perceived usefulness, perceived ease of use, perceived risk and information quality. Information quality itself had the highest influenced among others.
 
 Keywords: Perceived Usefulness; Perceived Ease of Use; Information Quality; Perceived Risks; Repayment Decision; Repayment Apps

Strategies to Improve Video Visit Use in Persons With Liver Disease

Strategies to Improve Video Visit Use in Persons With Liver Disease

Level Difficulty and Player Skill Prediction in Human Computation Games

Human computation games (HCGs) often suffer from low player retention. This may be due to the constraints placed on level and game design from the real-world application of the game. Previous work has suggested using player rating systems (such as Elo, Glicko-2, or TrueSkill) as a basis for matchmaking between HCG levels and players, as a means to improve difficulty balancing and thus player retention. Such rating systems typically start incoming entities with a default rating. However, when applied to HCGs, incoming entities may have useful information associated with them, such as player behavior during tutorials and properties of the tasks underlying the levels. In this work, we examined using features derived from player behavior and level properties to predict their eventual Glicko-2 ratings in the HCG Paradox. We found that using regression produced rating estimates closer to the actual ratings than default or baseline average ratings. The use of rating systems allows a unified approach to predicting both player skill and level difficulty.

Prevalence of multi-drug resistant tuberculosis and factors associated with treatment outcome in three districts of Himachal Pradesh, India

Background: Tuberculosis (TB) is a leading cause of morbidity and mortality in the world and especially in India. MDR TB has emerged as a major challenge in TB control in India. The prevalence of MDR TB is on the rise in India. We did this study to see the prevalence of MDR TB in 3 districts of Himachal Pradesh and to study the factors affecting treatment outcomes.Methods: The period of the study was from January 2019 to December 2019. We collected data of MDR TB patients from district TB offices of Solan, Bilaspur and Hamirpur. Information from patients was collected through questionnaires while interviews were conducted with district project officer and other health officials and documents, brochures, etc. were collected from Solan, Hamirpur and Bilaspur.Results: Overall prevalence rate of MDR TB in our study was 3.14%. Among new TB cases, the prevalence rate of MDR TB was 2.42% (81/3345), and among old treated cases was 9% (37/411). 31% MDR TB patients were old treated while 69% patients had no previous treatment history. The default rate was low in our study (0.84%) while 8 (6.4%) patients had treatment interruption but for less than 15 days.Conclusions: The prevalence in our study is comparable to the national drug resistance survey. Early detection, timely treatment and contact tracing is vital in decreasing the MDR TB cases. Better supervision, constant monitoring, proper counselling and timely identification and management of adverse effects of the drugs will improve the treatment outcome among MDR TB patients.

Do banks price environmental transition risks? Evidence from a quasi-natural experiment in China

This paper assesses the risk arising from transition toward a low-emission economy and examines its transmission channels within the financial system. The environmental dynamic stochastic general equilibrium (E-DSGE) model shows that tightening environmental regulation impairs firms' balance sheets in the short term, as it enforces firms to internalize the pollution costs, which consequentially escalates the risks facing the financial system. For the empirical analysis, we employ the Clean Air Action that the Chinese government launched in 2013 as a quasi-natural experiment. The analysis on a unique dataset containing more than one million loans indicates that the default rates of high-polluting firms rose by around 80% along their environmental policy exposure. Further analysis shows those joint equity commercial banks with lower degree of government intervention and better corporate governance structure were able to appropriately manage their exposure to transition risks, while the state-owned banks failed to factor in such risks when extending credit to the borrowers targeted by the environmental regulation.

The consequences of student loan credit expansions: Evidence from three decades of default cycles

This paper studies the link between credit availability and student loan repayment using administrative federal student loan data. We demonstrate that policy-driven changes in credit available to high-default institutions explain almost all of the historical time-series variation in defaults. Between 1981 and 1988, eligibility for federal loans was expanded, leading to the entry of institutions with borrowers more likely to default. From 1988 to 1992, credit access was tightened, leading to the exit of many institutions with high default rates. After 1992, the cycle was repeated, with credit access gradually loosened by unwinding many of the pre-1992 reforms.

Post COVID-19 financial distress in Pakistan: Prediction of corporate defaults at Pakistan Stock Exchange

COVID-19 has slowed down the global economic activity. This slowdown is expected to turn into an economic recession, where firms are expected to experience financial distress leading to corporate defaults. Predicting such defaults is important to safeguard the stakeholders’ interest in the financial markets. This study has estimated the extent of financial distress among the firms listed at PSX and constituting KSE 30 index, by using Altman’s Z-Score. The score has been computed using the financial statements of year 2019-20, and on the proforma financial statements for year 2019-20 and 2020-21. These financial years are considered as pre and post COVID-19 closing dates respectively for the financial statements. The proforma financial statements have been drawn for financial year 2019-20 and 2020-21 using established accounting conventions of prudence, conservatism, substance over form, and foreseeable future. The results of Z-score in pre and post COVID-19 have been compared to assess the change in degree of financial distress among the selected firms. A significant increase in the degree of financial distress has been observed, which may lead to an increased number corporate default for the firms listed at PSX. The firms and corporate regulators need to curtail the rate of corporate defaults.

Institutional and State-Level Factors Related to Paying Back Student Loan Debt Among Public, Private, and For-Profit Colleges

In this study, we examine whether institutional-level characteristics, student demographics, and state conditions are associated with student loan repayment rates and cohort-level loan default rates. We separately explore these characteristics for each of four higher education sectors: public 2-year colleges, for-profit colleges, public 4-year colleges, and private 4-year colleges. We conduct a series of multiple linear regressions on a sample of 2,375 colleges. Estimates suggest that across all sectors except at for-profits, colleges enrolling a higher percentage of historically underrepresented students, including first-generation and African American/Black students, report lower repayment rates. Additionally, a higher percentage of students enrolled who file as independents for tax purposes, as well as lower levels of family incomes among independent students, and lower graduation rates are all associated with lower repayment rates, across all four sectors. With the exception of for-profit colleges, the factors associated with higher cohort default rates include a smaller percentage of Asian students, a larger percentage of first-generation students, and lower median household incomes at the state level. Factors related to one-year and five-year loan repayment rates are similar, indicating that students who struggle to make progress on repaying their loans soon after leaving college continue to struggle in the future.