Loan Default Research Articles

Credit counselling: a contemporary strategy for survival of micro small and medium-sized enterprises in under-developed financial markets post COVID-19 pandemic

PurposeThe main purpose of this paper is to establish the mediating effect of credit counselling in the relationship between access to microcredit and survival of micro small and medium-sized enterprises (MSMEs) in developing countries in sub-Saharan Africa post COVID-19 pandemic with data collected from rural Uganda.Design/methodology/approachStructural equation modelling (SEM) through SmartPLS 4.0 was used to generate the standardized parameters to test whether credit counselling mediates the relationship between access to microcredit and survival of MSMEs in developing countries in sub-Saharan Africa post COVID-19 pandemic with data collected from rural Uganda.FindingsThe SEM bootstrap results revealed that credit counselling enhances access to microcredit by 27% to promote survival of MSMEs in developing countries in sub-Saharan Africa post COVID-19 pandemic with data collected from rural Uganda.Research limitationsThe current study focused only on women MSMEs. Future studies may possibly collect data from all the MSMEs to draw better generalization of the findings within the sector.Practical implicationsThe findings can help public finance policy to ensure provision of credit counselling to microentrepreneurs who borrow from different financial institutions to reduce the problem of loan defaults and delinquency rampant in lending. This could be done through conducting routine business education and counselling sessions for microentrepreneurs who often need credit to grow their businesses.Originality/valueThis study is amongst the first few studies to establish the mediating effect of credit counselling in the relationship between access to microcredit and survival of MSMEs in developing countries in sub-Saharan Africa in the aftermath of COVID-19 pandemic with data collected from rural Uganda. There is a dearth in literature and theory on the rehabilitative and preventive role of credit counselling in reducing repayment defaults amongst borrowers within the credit market to spur survival of MSMEs seen as the main enabler of economic growth, especially in developing countries. In fact, credit counselling acts as a safety net by substituting financial literacy and education to solve the rampant problem of overindebtedness amongst borrowers who are debt illiterate within the credit market.

Determinants of Repayment for Youth Development Fund Loans in Tanzania

The study intended to identify factors influencing youth group loan repayment for the Youth Development Fund (YDF) in Tanzania. This study utilizes cross-sectional primary data from structured questionnaires and interviews with 388 individuals in the Butiama District of the Mara Region. The analysis involved descriptive statistics and econometric estimation using the ordinal logistic regression model. As per descriptive analysis results, this study provides a comprehensive analysis of socio-economic and institutional factors distinguishing loan defaulters from non-defaulters among youth participants in Tanzania's loan programs. Significant variations, supported by p < 0.05, highlight that non-defaulters possess more leadership experience, exhibit superior business performance, undergo increased training, and are in closer proximity to lending institutions. The study reveals noteworthy differences in demographic factors such as marital status, education, belief in societal employment, and types of financed projects between the two groups. Misappropriation of funds, peer pressure, and economic shocks are identified as key contributors to loan defaults, emphasizing the need for targeted interventions and vigilant monitoring for enhanced loan repayment and project success. Moreover, the econometric estimation using the ordinal logistic regression model, with statistical significance at a 5% level, demonstrates that independent factors account for 85.5% of the variation in loan repayment performance among youth groups. The study encounters societal views favoring employment over entrepreneurship, revealing a negative impact on youth loan repayment. Factors like loan diversion, peer pressure, group size, and entrepreneurship training emerge as significant determinants of loan repayment performance, providing valuable insights. In conclusion, the study underscores the critical importance of tailored interventions for addressing challenges faced by youth borrowers. Misappropriation of funds, peer pressure, and economic shocks require nuanced mitigation strategies. Policy recommendations encompass refining loan structures, enhancing training programs, and challenging societal norms favoring employment over entrepreneurship. Tailored interventions with strengthened monitoring, addressing socio-cultural attitudes, and internal control reinforcement are suggested for sustained success in youth-focused financial initiatives through continuous research and policy adaptation.

EFFECTS OF NON-PERFORMING LOANS ON RETURN ON ASSETS OF SELECTED COMMERCIAL BANKS IN NIGERIA

The essence of creating Commercial banks is to act as intermediary between the surplus (supply side) and deficit (demand side) units of funds. This is, in addition to other crucial objectives of profitability, growth in assets, and growth in customer base. To achieve these objectives, commercial banks grant loans to individuals, business organizations and governments among other profit yielding ventures. Loan defaults could be rampant resulting from low quality of assets, high non-performing risk assets (credit risk) that may result in huge loan losses, and reduction in bank profitability leading to adverse effect on return on assets of commercial banks. This study investigates the effect of non-performing loans (using loan loss provision as proxy) on return on assets of selected commercial banks in Nigeria. Annual time series secondary data from 2010 to 2021 were sourced from Central Bank of Nigeria Statistical Bulletin and the Annual Financial Reports of affected bank, and panel least square method of analysis was used as our estimation technique. The results revealed that while loan loss provision has negative impact on return on assets of the selected commercial banks, interest rate has a positive impact on return on assets of the selected commercial banks within the period of study. The study recommends, amongst others that effective credit policy, reflected in flexible tenure; restructuring of credit terms; and conversion should be adopted in the selected commercial banks to minimize the negative impacts of loan defaults. This is because decline in loan default will lead to reduction in non-performing loans, and subsequently boost return on assets of affected banks in Nigeria.
 Keywords: Non-Performing Loan, Return on Asset, Interest Rates, Bank Performance, Bank Profitability, and Loan Loss Provisions.

A spatial–temporal graph-based AI model for truck loan default prediction using large-scale GPS trajectory data

With the increasing uncertainties in freight transportation, truck loans are playing a crucial role in the stability and development of the logistics industry. A pivotal problem to truck loan management is controlling credit risk. Much research has focused on pre-loan default prediction considering applicants’ static personal information. However, few studies concentrate on post-loan risk control, which needs to track customers’ dynamic behaviors to ensure the loans can be recovered. We propose a Spatial–temporal Graph-based AI model for Truck loan Default prediction (SGTD) in post-loan management, which considers spatial and temporal dependencies of customers’ dynamic behaviors using large-scale GPS trajectory data. (1) The spatial dependency is caused by the functional similarity of the trajectories, which is only relevant to people’s travel habits and purposes. Therefore, we detect stay points from the nationwide trajectories and represent them with point-of-interest (POI) information as POI nodes. Then, we learn the latent vectors of POI nodes in a heterogeneous network through metapath learning, which are utilized to initialize vectors in modeling the temporal dependencies. (2) To learn temporal dependencies, we split the long trajectory into period sequences according to the cyclical nature of truck loans. Then we model each sequence with graph neural networks to capture the short-term closeness and period dependencies. Finally, we introduce LSTM networks to learn the long-term trend dependency and predict the default probability. The outcomes of extensive experiments on a real-world dataset demonstrate the effectiveness of SGTD in AUC, KS, accuracy, and F1-score, as well as the practical economic contribution. The results also implicate that GPS data can contribute to learning drivers’ socioeconomic information, and the proposed method is applicable to other transportation scenarios.

Research on Credit Risk Evaluation In Commercial Banks In China

Credit loans of Chinese commercial banks have been increasing; however, the risk of loan default is also rising rapidly, and Chinese commercial banks have been paying more and more attention to the management of credit risk, and that leads to the commercial banks’ exploration of the credit risk evaluation. Moreover, data mining and mathematical statistics techniques are applied in credit rating, risk monitoring, and prediction of customer default risk.

Accuracy Comparison between Five Machine Learning Algorithms for Financial Risk Evaluation

An accurate prediction of loan default is crucial in credit risk evaluation. A slight deviation from true accuracy can often cause financial losses to lending institutes. This study describes the non-parametric approach that compares five different machine learning classifiers combined with a focus on sufficiently large datasets. It presents the findings on various standard performance measures such as accuracy, precision, recall and F1 scores in addition to Receiver Operating Curve-Area Under Curve (ROC-AUC). In this study, various data pre-processing techniques including normalization and standardization, imputation of missing values and the handling of imbalanced data using SMOTE will be discussed and implemented. Also, the study examines the use of hyper-parameters in various classifiers. During the model construction phase, various pipelines feed data to the five machine learning classifiers, and the performance results obtained from the five machine learning classifiers are based on sampling with SMOTE or hyper-parameters versus without SMOTE and hyper-parameters. Each classifier is compared to another in terms of accuracy during training and prediction phase based on out-of-sample data. The 2 data sets used for this experiment contain 1000 and 30,000 observations, respectively, of which the training/testing ratio is 80:20. The comparative results show that random forest outperforms the other four classifiers both in training and actual prediction.

Bank Lending Rates and Nonperforming Loans of Listed Commercial Banks in Kenya

The rise in non-performing loans in Kenyan commercial banks over the past ten years has created instability in the financial industry. Due to their impact on borrowers' ability to repay the loans, high-interest rates are a contributing factor to non-performing loans. The interest rate on loans has an implicit cost that is inherent to bank credit and has an impact on loan defaults. In this sense, a large percentage of non-performing loans (NPLs) in Kenya’s Commercial banks has continued to impede economic expansion due to high default rates experienced by many banks making them unable to advance new loans. The study investigated the influence of bank lending rates on nonperforming loans in listed commercial banks in Kenya using secondary monthly data from November 2019 when the interest capping was repelled to September 2023. Secondary data was obtained from the Central Bank of Kenya Monthly Statistical Bulletin. Inferential statistics using regression analysis was utilized to analyze the data. The regression model results showed that bank lending rates had a positive and statistically significant effect on non-performing loans of listed commercial banks in Kenya as illustrated by a P-value of 0.0000000054, which is less than 0.05. The results implied that lending rates do influence non-performing loans of listed commercial banks in Kenya as measured by the gross non-performing loans ratio. Policymakers in Kenya need to control lending rates to reduce the rising rates of non-performing loans.

Identification of Most Preferable Machine Learning Technique for Prediction of Bank Loan Defaulters

Identification of Most Preferable Machine Learning Technique for Prediction of Bank Loan Defaulters

Design and implementation of a loan default prediction system using random forest algorithm

Loan default prediction is a crucial task in the lending industry; it helps financial institutions make informed decisions about granting loans. It is usually a daunting task for the bank or financial institution to predict customers who will default on a loan especially when there are thousands of applicants. This loan default prediction system aimed to improve the Area Under the Curve (AUC) score. This loan default prediction system used various data sources, such as demographic information, credit history, and financial performance to predict the likelihood of a loan being defaulted. The system used a random forest (RF) machine learning algorithm to analyze the data and build predictive models. The model was then used to make predictions about new loan applicants and existing borrowers who may default in the future. The system can be customized to meet the specific requirements of different lending institutions. The system enables lenders to make better decisions on loan approval, interest rate determination, and credit risk, management. The loan default prediction system also provides insights into risk factors that contribute to loan default and helps lenders develop effective strategies to mitigate these risks, making it an indispensable tool for lenders. The resultant system achieved an improved AUC score of 98%.

The performance of Islamic and conventional microfinance loans in Afghanistan: The Taliban and beyond

The Afghanistan microfinance industry has witnessed laurels in recent decades, making it crucial to closely monitor its development and sustainability. This study aims to assess the performance of Islamic and conventional microfinance loans in two aspects: first, by examining the occurrence of loan defaults, and second, by distinguishing between loan performance in areas controlled by the Taliban and those outside their control. To accomplish this, we analyze a unique dataset comprising over 9500 borrowers linked to an Afghan microfinance institution during the period spanning from January 2017 to February 2020. Our findings reveal that regions under Taliban control experienced fewer instances of loan defaults compared to areas not under their influence. Additionally, our analysis indicates that borrowers of Islamic loans in Afghanistan default more frequently than borrowers of conventional loans, particularly in Taliban-controlled areas. These observations remain largely consistent when we explore the factors influencing the number of days overdue on loan repayments. Furthermore, our conclusions find further support from both of the microfinance institution's Islamic loan products: business Murabaha loans, available to individuals of any gender, and women Murabaha loans, created as a group lending scheme exclusively for women. The results from this study offer valuable insights into how policy discussions in Afghanistan should be adjusted to facilitate a smooth transition for the microfinance industry.

Research on loan default prediction based on logistic regression, randomforest, xgboost and adaboost

Lenders often experience loan defaults, resulting in huge losses to lenders. Lenders are required to conduct a credit assessment of borrowers before making loans. Machine learning plays an essential role in loan credit analysis. This study analyzes the application of machine learning in loan credit analysis through a dataset of borrowers from Kaggle and looks for an excellent algorithm.This study use Logistic Regression, randomforest, XGBoost and AdaBoost to fit the dateset and compare their accuracy in prediction.In terms of results, XGBoost performed well while logistic regression performed poorly. For banks or lending institutions, using Gradient Boosting Decision Tree like XGBoost to predict loan default can increase profit.

An improved Approach for Sustainable Risk Management Practices in Modern Businesses

The complexity of the business environment, which is shaped by dynamic economic, environmental, and social factors, makes it more important for companies to develop sustainable risk management practices. This research proposes a novel approach that combines traditional methods with modern machine learning techniques in response to the complex challenges faced by contemporary businesses in balancing risk and sustainability. Our study uses a public bank loan default dataset as a case study to address missing data systematically through robust imputation mechanisms and transform categorical variables using feature encoding. Spearman correlation analysis helps us understand complex variable relationships and guides subsequent feature selection. The decision tree classifier, a powerful machine learning algorithm known for its interpretability, is applied to identify key factors contributing to risk assessment. The hierarchical structure of the decision tree not only reveals important variables but also provides an explicit representation of the decision-making process. ROC curve analysis shows how well our predictive model can differentiate potential loan defaults.

Extraneous Shocks Affecting Agribusiness Loans Default Rate in Agricultural Finance Corporation, Mount Kenya Region

Extraneous Shocks Affecting Agribusiness Loans Default Rate in Agricultural Finance Corporation, Mount Kenya Region

The Crystal Ball of Finance : A Comparative Study of Predictive Models for Loan Default

The Crystal Ball of Finance : A Comparative Study of Predictive Models for Loan Default

EFFECTIVE LOAN MANAGEMENT AND FINANCIAL PERFORMANCE OF COMMERCIAL BANKS IN RWANDA

The aim of this study was to find out the effective loan management and financial performance of commercial banks in Rwanda. The methodology employed descriptive and correlational designs. Purposive sampling technique was applied to select sampled respondents. Data was analyzed using descriptive statistics, spearman correlation, regression analysis and financial ratios analysis. After all the following findings were reached: The effectiveness of loan management was measured by loans planning, client appraisal and collection policy. Qualitatively analysis was used to explain the effectiveness of loan management and financial performance. According to the financial performance considering the overall mean of 3.97 which is interpreted as high mean. The quantitative results that were examined through the financial statements of Bank of Kigali Group Plc in form of profitability ratio, liquidity ratios and efficiency. To the third specific objective, it was found that there is high correlation between loan management and profitability of Bank of Kigali Group Plc. This is explained by the correlation (r) of 0.734 which is interpreted as high and the p-value of 0.00 which is less that the significance value of 0.01. Based on findings have provide the suggestions to BK Plc: It is suggested that periodically relevant training programs are organized for credit officers particularly in the area of risk management, management of loan default and financial analysis. This helps improve the knowledge and analytical skills of the loan officers so as to improve their client’s appraisal techniques. The credit committees at all levels must review clients’ bank statements in order to ensure that credit is collected in a timely manner. The Bank of Kigali Plc should check at the character of the borrower during loan review and to enhance their loan risk control.

An IV Hazard Model of Loan Default with an Application to Subprime Mortgage Cohorts

An IV Hazard Model of Loan Default with an Application to Subprime Mortgage Cohorts

Towards a Machine Learning-based Model for Corporate Loan Default Prediction

Towards a Machine Learning-based Model for Corporate Loan Default Prediction

Credit scoring using machine learning and deep Learning-Based models

<abstract><p>Credit scoring is a useful tool for assessing the capability of customers repayments. The purpose of this paper is to compare the predictive abilities of six credit scoring models: Linear Discriminant Analysis (LDA), Random Forests (RF), Logistic Regression (LR), Decision Trees (DT), Support Vector Machines (SVM) and Deep Neural Network (DNN). To compare these models, an empirical study was conducted using a sample of 688 observations and twelve variables. The performance of this model was analyzed using three measures: Accuracy rate, F1 score, and Area Under Curve (AUC). In summary, machine learning techniques exhibited greater accuracy in predicting loan defaults compared to other traditional statistical models.</p></abstract>

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

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

Thermal stress and financial distress: Extreme temperatures and firms’ loan defaults in Mexico

The frequency and intensity of extreme temperature events are likely to increase with climate change. Using a detailed dataset containing information on the universe of loans extended by commercial banks to private firms in Mexico, we examine the relationship between extreme temperatures and credit performance. We find that unusually hot days increase delinquency rates, primarily affecting the agricultural sector, but also non-agricultural industries that rely heavily on local demand. Our results are consistent with general equilibrium effects originated in agriculture that expand to other sectors in agricultural regions. Additionally, following a temperature shock, affected firms face increased challenges in accessing credit, pay higher interest rates, and provide more collateral, indicating a tightening of credit during financial distress.