Competitive Conditions in Nepalese Commercial Banks

This study aims to analyze the competitive conditions of commercial banks in Nepal. Competition is measured through structural and non-structural measures of bank competition. Data were taken from 21 commercial banks of Nepal using pooled sampling method, including five commercial banks based on the highest total assets and sixteen commercial banks using random sampling. Concentration ratio, Herfindahl-Hirschman Index, H-statistic and Lerner Index measures were used to assess the competitive position of Nepalese commercial banks. Panel data regression model with bank fixed effect and time fixed effect was employed to measure H-statistic and Lerner index. Findings showed the increasing pattern of capitalization and the decreasing trend of non-performing loan ratio, indicating that Nepalese commercial banks have a low possibility of loan default and, are more financially stable. It also showed the declining trend of bank concentration and HHI, suggesting that Nepalese commercial banks are losing their monopoly power and becoming more competitive in recent years. Competition in the loan market was found higher than deposit market competition. Banks have to pay special attention to loan portfolio management rather than deposit collection strategies. This study concludes that the competitive condition of Nepalese commercial banks is monopolistic. Therefore, appropriate strategies might be taken into action to sell financial products and services competitively.

COVID-19 pandemic risk and probability of loan default: evidence from marketplace lending market

As the COVID-19 pandemic adversely affects the financial markets, a better understanding of the lending dynamics of a successful marketplace is necessary under the conditions of financial distress. Using the loan book database of Mintos (Latvia) and employing logit regression method, we provide evidence of the pandemic-induced exposure to default risk in the marketplace lending market. Our analysis indicates that the probability of default increases from 0.056 in the pre-pandemic period to 0.079 in the post-pandemic period. COVID-19 pandemic has a significant impact on default risk during May and June of 2020. We also find that the magnitude of the impact of COVID-19 risk is higher for borrowers with lower credit ratings and in countries with low levels of FinTech adoption. Our main findings are robust to sample selection bias allowing for a better understanding of and quantifying risks related to FinTech loans during the pandemic and periods of overall economic distress.

Machine Learning application for selecting efficient Loan Applicants in Private Banks of Bangladesh

Machine Learning Applications have been well accepted for various financial processes throughout the world. Supervised Learning processes for objective classification by Naïve Bayes classifiers have been supporting many definitive segregation processes. Various banks in Bangladesh have found challenging moments to identify financially and ethically qualified loan applicants. In this research process, we have confirmed the safe applicant’s list using definitive variable measures through identifiable questions. Our research process has successfully segregated the given applicants using Naïve Bayes classifier with the proof of lowering loan default rate from an average of 23.26%% to 11.76% and development of financial ratios as performance indicators of these banks through various financial ratios as indicators of these banks.

P2P Lending platforms in Malaysia: the awareness among young adults

Background - Since 2016, the Securities Commission (SC) in Malaysia has given licenses to only eleven P2P lending platforms. Such lending platforms are expected to disrupt the lending services of traditional lenders in the coming years. However, being still in their infant stages, it is essential to know the extent to which such platforms are made known to potential investors out there. This study examines the extent to which young adults are aware of Malaysia's eleven P2P lending platforms. Methods - A sample of 65 undergraduate students majoring in finance and accounting was used for this pilot study. An online questionnaire was designed with three main parts: demographic, financial literacy, and P2P lending awareness. Results - Findings show that more than half of respondents in the sample are not aware of P2P lending platforms in Malaysia. Most of the respondents are financially literate to certain degrees. Those aware of their presence underestimated the potentially high level of their default rates and misunderstood that investor would be fully protected by such platforms when a loan default. Conclusions -The study's findings have shed light on the current awareness of P2P lending platforms among Malaysian young adults, potential investors of such platforms in the coming years.

AN EMPIRICAL STUDY ON LENDING PATTERN AND PERCEPTION OF COMMERCIAL BANKS TOWARDS SMALL AND MEDIUM ENTERPRISES LENDING

Micro, Small and Medium sector (MSME) is the most dynamic sector due to its capacity for innovation, competence for advancements and perseverance to face multiple challenges. Though this sector contributes 8 per cent to GDP, 45 per cent towards manufacturing output and 40 per cent of exports. Still, they lack support to grow exponentially. Understanding the lending pattern of the banks and the perception of commercial banks towards MSME lending is essential not only for academic knowledge but also could necessitate policy changes. The objective of the research is to understand the lending pattern of banks to SMEs and the perception of commercial banks towards SME lending. The researcher used both primary and secondary data. Statistical tools ANOVA and Kruskal Wallis tests are used to analyze the collected data. The results indicate that there is no significant difference in the lending pattern of public sector banks, Private sector banks and foreign banks and the bank staff have a similar level of perception on difficulties in lending to SMEs, perception on the rejection of loan, perception on reluctance to serve SME sector, perception on loan default and factors affecting credit decisions irrespective of the type of the bank and experience in SME lending.

Loan default prediction of Chinese P2P market: a machine learning methodology

Repayment failures of borrowers have greatly affected the sustainable development of the peer-to-peer (P2P) lending industry. The latest literature reveals that existing risk evaluation systems may ignore important signals and risk factors affecting P2P repayment. In our study, we applied four machine learning methods (random forest (RF), extreme gradient boosting tree (XGBT), gradient boosting model (GBM), and neural network (NN)) to predict important factors affecting repayment by utilizing data from Renrendai.com in China from Thursday, January 1, 2015, to Tuesday, June 30, 2015. The results showed that borrowers who have passed video, mobile phone, job, residence or education level verification are more likely to default on loan repayment, whereas those who have passed identity and asset certification are less likely to default on loans. The accuracy and kappa value of the four methods all exceed 90%, and RF is superior to the other classification models. Our findings demonstrate important techniques for borrower screening by P2P companies and risk regulation by regulatory agencies. Our methodology and findings will help regulators, banks and creditors combat current financial disasters caused by the coronavirus disease 2019 (COVID-19) pandemic by addressing various financial risks and translating credit scoring improvements.

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.

Analysis of Loan Availability using Machine Learning Techniques

In the banking system, banks have a variety of products to provide, but credit lines are their primary source of revenue. As a result, they will profit from the interest earned on the loans they make. Loans, or whether customers repay or default on their loans, affect a bank's profit or loss. The bank's Non-Performing Assets will be reduced by forecasting loan defaulters. As a result, further investigation into this occurrence is essential. Because precise forecasts are essential for benefit maximisation, it's crucial to analyse and compare the various methodologies. The logistic regression model is an important predictive analytics tool for detecting loan defaulters. In order to assess and forecast, data from Kaggle is acquired. Logistic Regression models were used to calculate the various performance indicators. The models are compared using performance metrics like sensitivity and specificity. In addition to checking account details (which indicate a customer's wealth), the model is significantly better because it includes variables (customer personal attributes such as age, objective, credit score, credit amount, credit period, and so on) that should be considered when correctly calculating the probability of loan default. As a result, using a logistic regression approach, the appropriate clients to target for loan issuance can be easily identified by evaluating their plausibility of loan default. The model implies that a bank should assess a creditor's other attributes, which play a critical role in credit decisions and forecasting loan defaulters, in addition to giving loans to wealthy borrowers.

Exploring the sources of loan default clustering using survival analysis with frailty

This paper investigates whether three microeconomic loan characteristics are sources of loan default clustering in the Mexican banking sector by employing survival analysis with frailty. Using a large sample of bank loan level data granted to micro, small and medium sized firms from January 2010 to 2018, we test whether classifying loans by the bank's systemic importance, industry or at individual firm level enhances the predictions of loans defaults. Our results show that loans granted by Domestic Systemically Important Banks contribute to the default clustering in micro and small firm loans. This is due to aggregate default rate levels and clusters that are large for these firms loans compared with loans provided to medium-sized firms. These findings have important implications for bank's expected loss management related to the correlated loan default risk

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.