An Empirical Study on Determinants of Loan Repayment Behaviour of SMEs Towards Commercial Banks

India, as a developing economy even after five decades of planning, still has a long way to catch up with the advanced economies of the globe. The goal may be distant, but surely, the time needed to reach can be reduced by accelerating the pace of development. One way of doing this is by the development of industrial and business ventures. There exists a positive relationship between the growth of an economy and the growth of small and medium enterprises (SMEs). SMEs play a vibrant role in the development of an economy. Access to the formal source of credit by entrepreneurs is essential in a growing economy. Lending to SMEs is a risky activity for the banks as repayment of these loans are less guaranteed. The research article attempts to analyse the factors influencing the loan repayment behaviour of SMEs towards commercial banks. Data are collected using the convenience method of sampling from 80 registered SMEs belonging to the manufacturing and service sectors in the Bangalore region, and data are analysed using statistical tools such as correlation and logit regression analysis, conclusions are drawn based on these findings. The study reveals that characteristics of loan and lender influence the repayment to the maximum extent. The findings are helpful for commercial banks in redesigning suitable policies and schemes to reduce loan defaults.

Evaluating Default Risk and Loan Performance in UK Peer-to-Peer Lending: Evidence from Funding Circle

The United Kingdom is the third-largest peer-to-peer (P2P) lending market in the world, which is surpassed only by the two dominant forces in P2P investing, China and the United States of America. As an innovative financial market in the UK, P2P lending brings not only many opportunities but also many risks, especially the loan default risk. In this context, this paper uses binary logistic regression and survival analysis to evaluate default risk and loan performance in UK P2P lending. The empirical results indicate that credit group, loan purpose for capital needs, sector type, loan amount, interest rate, loan term, and the age of the company all have a significant impact on the probability of loan default. Among them, the interest rate, loan term, and loan purpose for capital needs are the three most important determinants of the probability of loan defaults and survival time of loans.

An Analysis of the Unpredictable Factors Influencing Loan Default in Small Scale Enterprises in the Port Loko Municipality in Sierra Leone using the Fish Bone Diagram

The study shows that there are unpredictable factors influencing loan default in small-scale enterprises in Port Loko Municipality. A fishbone diagram which is a cause an effect tool was used to determine these factors. A brainstorming activity was used to get the views of participants with regard to the Research Question. The research question was to respond to a research objective which was “Are there unpredicted factors influencing loan default in small scale enterprises in Port Loko Municipality in Sierra Leone?”. Reviews of necessary literature were done to aid the study. In the review, matters relating to loan default and possible causes were addressed. It is unfolded that there are some loan defaults that are as a result of the borrowers’ lapses and others that are lender-oriented causes. The population size of one hundred and a random sample size of sixty people were used as participants to carry out the brainstorming activity. The population is comprised of small-scale enterprise owners and workers of credit or Microfinance institutions in the Municipality. Brainstorming participants proved that the death of clients or borrowers, internal insecurity, outbreak of diseases (Pandemic), Natural Disasters, and accident all significantly influence loan default of small-scale enterprises.

Why segmentation matters: a Machine Learning approach for predicting loan defaults in the Peer-to-Peer (P2P) Financial Ecosystem

Peer-to-Peer (P2P) lending is an online lending process allowing individuals to obtain or concede loans without the interference of traditional financial intermediaries. It has grown quickly the last years, with some platforms reaching billions of dollars of loans in principal in a short amount of time. Since each loan is associated with the probability of loss due to a borrower's failure, this paper addresses the borrower's default prediction problem in the P2P financial ecosystem. The main assumption, which makes this study different from the available literature, is that borrowers sharing the same homeownership status display similar risk profile, thus a model per segment should be developed. We estimate the Probability of Default (PD) of a borrower by using Logistic Regression (LR) coupled with Weight of Evidence encoding. The features set is identified via the Sequential Feature Selection (SFS). We compare the forward against the backward SFS, in terms of the Area Under the Curve (AUC), and we choose the one that maximizes this statistic. Finally, we compare the results of the chosen LR approach against two other popular Machine Learning (ML) techniques: the k Nearest Neighbors (k-NN) and the Random Forest (RF).

How Does the Urgency of Borrowing in Text Messages Affect Loan Defaults? Evidence from P2P Loans in China

In P2P loans with information asymmetry, the text information described by the borrower plays an important role in alleviating the information asymmetry between borrowers and lenders. To explore the borrowing described in text information and its relationship with default behavior, this article selects credits from April 2014 to October 2016 as the repayment period and studies default data. This is performed based on the length of the excavated text, purpose of the loan, repayment ability, willingness to reimburse, five text variables, and degree of loan urgency. The empirical results show that text length has a significant negative correlation with the default probability of borrowers. Different loan purposes have different default risks. Interestingly, the more urgent a loan is, the more likely the borrower is to default. However, repayment ability information and repayment willingness information have no significant effect on default behavior. In addition, the Nagelkerke R2 improved by nearly 3% in the logistic regression model with the addition of text variables. In short, fully excavating loan description information is helpful in reducing the risk of loan default.

An Online Credit Agreement with Collateral in Indonesia

Along with the rapid development of information technology which in turn has an impact on economic activity around the world, financial technology is here to provide facilities in the provision of financial services for the community. The ease of convenience offered through unsecured online loans carries the risk of loan defaults made by debtors. The purpose of this research is to find out what is the urgency of the need for collateral as one of the requirements for submitting online loans and how online registration of fiduciary collaterals can be a preventive measure for bad credit in online credit distribution. This study uses a normative-juridical research method. The result of this research is that there is a collateral that the online lending agreement functions as a legal umbrella so that debtors' obligations to creditors are fulfilled so that they avoid default, default, and even investor losses in Fintech P2P Lending activities. The importance of having a fiduciary collateral that is registered in the credit agreement online is to avoid a legal vacuum (rechts vacuum) or legal vacuum (wet vacuum) when there is default or default by the debtor.

Causes of loan defaults within Microfinance institutions: Learning from micro and small business owners in Rwanda: A case of MSEs in Kigali

The study analyse the factors that causes loan defaults within Microfinance institutions learning from the perception of entrepreneurs in Rwanda. Explanatory research design was used. Data was collected from primary and secondary sources using questionnaire and documentation. The study population included microfinance institutions within Kigali. The target population included MSEs that are classified within a portfolio of nonperforming loans. Structural Equation Modeling (SEM) was used to analyse the correlation between the study variables. The findings from the survey showed that loan delay, loan shortage, loan deviation, interest rate, improper management, business environment have a significant impact on nonperformance. The researcher recommended that entrepreneurs should be trained on financial discipline and how to manage the loan finance

Predicting Loan Defaults Using Logistic Regression

We used anonymized data from a loan company to analyze correlations between loan defaults and other characteristics of loans or borrowers of loans. We performed an exploratory data analysis of the different factors and how they correlated with loan defaults. Using observations made in the EDA, we proceeded to use logistic regression to predict the odds of loan defaults with several loan characteristics as predictor variables. Different models were evaluated and cross-validated using AIC, AUC, and predicted accuracy. Weighted accuracy was also measured because the loan dataset was a stratified sample. We concluded that the interest rate most accurately predicted the odds of a loan default and that the most useful model was both simplistic and accurate. Research was limited by the variables that were not analyzed during EDA, the limited variables the loan dataset contained, and the modeling technique used.