Bank Loans Research Articles

Socio-Economic Status of Buffalo Farmers and Management Practices of Buffaloes at Shahjadpur Milk Pocket Area in Bangladesh

Buffalo is the most neglected species of large ruminant, despite contributing to safe and sustainable milk and meat production in Bangladesh. A survey was conducted at five unions of Shahjadpur upazilla under Sirajganj district which is called the milk pocket area in Bangladesh. The objective of the survey was to find out the socio-economic condition of buffalo farmers in these areas. Prior to conducting the survey work from February to June 2024, a questionnaire was designed and pre-tested. SPSS software was used to statistically analyse the collected data. The results revealed that the majority (84%) of buffalo farmers were male and mostly (72%) practised by middle-aged (30-45 years). The education level of farmers had no schooling (12%), primary (50%), secondary (22%), higher secondary (16%) and graduation (0%), respectively. In the survey area, a majority (76%) of the farmers reared young male (1.5 to 2 years) buffalo for fattening. Additionally, 72% of farmers raised buffaloes with intensive systems and the remaining 28% with semi-intensive. The involvement of respondents in farming, business and others were 84, 8% and 8% respectively. Most (60%) of the farmers dewormed their buffalo regularly but only 28% practised regular vaccination. The main factors limiting buffalo production in the study locations were the high price of concentrate feed (88%), lack of pond for wallowing (70%), lack of improved dairy buffalo breeds (60%), lack of green fodder (30%), unavailability of bank loan (18%) and training facility (24%). The net income from fattening one buffalo per half yearly was BDT 26713. It indicates that rearing buffaloes for fattening purposes in the selected areas was profitable.

PENGARUH KOMPETISI BANK TERHADAP STABILITAS SISTEM KEUANGAN PERBANKAN (STUDI KASUS PADA KBMI IV TAHUN 2019-2022)

The emergence of banking as an intermediary institution plays a very important role in the economy because it is the heart that must pump "money" and distribute it to all organs that need the money. Therefore, it is very important to maintain the stability of the banking financial system. The development of the banking system can trigger competition in the banking industry which can affect banking stability. This research was conducted to determine the effect of bank competition on the stability of the banking financial system. This research focuses on KBMI 4 data which is a large bank HHI index, market share and Loan to Deposit Ratio (LDR) used as a proxy for competition and Non-Performing Loans (NPL) as a proxy for banking financial stability. The analysis technique used is Panel Data Regression with KBMI 4th ​​quarter data for 2019-2022. Of these three variables, the HHI index and Loan to Deposit Ratio (LDR) variables have a significant effect on Non-Performing Loans (NPL). Meanwhile, the market share variable has no significant effect on Non-Performing Loans (NPL).

The Development of SME's in Pre and Post-Pandemic Nigeria: The Role of Banks

This research paper explores how banks have impacted the growth of small and medium enterprises (SMEs) in Nigeria from 1991 to 2021. The review of existing literature discussed the challenges faced by SMEs due to the COVID-19 pandemic, which resulted in disruptions to both demand and supply. These disruptions caused revenue losses and a decrease in market confidence, further worsening liquidity constraints. The study employed a pair Granger causality test to investigate if loans from banks to SMEs influenced SME development over the research period. The variables examined included the proportion of SME contribution to GDP as an indicator of SME progress, bank loans extended to SMEs, the inflation rate (INF), and the exchange rate (EXCR). The findings reveal that neither commercial bank loans nor inflation impacted SME development without any evidence of causation. However, there was a relationship between exchange rates and SME growth. These results suggest a lasting connection between the factors studied. These outcomes recommend strengthening collaboration between the government and commercial banks to increase the share of loans directed towards SMEs for their advancement.

Effect of Economic Risk Sources on the Quality of the Business Environment in the SME Segment: A Case Study from the Slovak Republic

The aim of the article is to define and quantify the impact of specific economic risk sources on the quality of the business environment (QBE). The subject of the analysis was the evaluation of the impact of selected economic risk sources, including the macroeconomic environment, monetary policy and interest rates, and corporate financing on the QBE. Data was collected between 2018 and 2022 using an online survey method. A total of 480 SMEs from the Slovak Republic’s business environment completed the questionnaire. Advanced statistical techniques, including descriptive statistics, correlation analysis, and linear regression analysis, were employed to evaluate the statistical hypotheses. The empirical results yielded several significant findings. All examined sources of the macroeconomic environment, such as the level of basic macroeconomic indicators, the state of the macroeconomic environment in the context of starting a business, and innovation activities of SMEs, determine the QBE. Other key factors that positively influence the quality of the business environment include: stabilization of the business environment by the central bank's monetary policy; the level of bank interest rates on the innovation activities of companies and availability of bank loans. The findings are significant not only for the SME segment itself and their owners and top managers but also for national-level institutions along with non-profit organizations assisting in improving the entire SME segment in the business environment.

Beyond banks: Navigating the shift to peer-to-peer lending for small enterprises

This study sheds light on how non-traditional lending avenues, specifically peer-to-peer (P2P) lending platforms, influence the financing decisions of small businesses. It introduces a theoretical framework where borrowers weigh the option between opting for a cost-effective traditional bank loan versus an expensive option through crowdlending platforms. The findings suggest that crowdlending platforms become a more appealing choice in the event of credit supply disruptions in traditional banking sectors. Leveraging the phased introduction of mobility restrictions during the COVID-19 pandemic as a case study, our research demonstrates a noticeable pivot of small businesses towards alternative funding sources, such as P2P lending. These findings emphasize the value of offering a variety of financial tools to small businesses so they can weather economic storms.

Fintech model performance analysis in credit loans: evidence from China city commercial banks

ABSTRACT The analysis of bank loan performance has consistently been a central issue in banking research. With the impact of the COVID-19 pandemic, we have witnessed a significant increase in online loans. Our study employs fintech models to develop a big data credit analysis system, evaluating model efficiency and the effect of ‘data organization’ on predictive performance for city commercial bank loans. Our results show that training data size, timeliness, and inter-bank ‘data sharing’ significantly affect model predictions. We also find that traditional econometric models (Logit and Probit) have the weakest predictive ability. However, ‘most advanced’ deep learning models perform worse than machine learning models with lower algorithmic complexity, such as Random Forest. Besides, ‘sharing training data’ generally increases model efficiency, but there are exceptions for larger city commercial banks. This paper provides ample empirical evidence for the application of the fintech model in bank loan credit. It highlights several characteristics of training data, such as size and timeliness, which greatly affect model efficiencies. We also provide policy recommendations for fintech transition and collaboration among municipal banks.

The effects of FinTech adoption on bank loan spreads

PurposeThis paper aims to examine the impact of traditional banks’ financial technology (FinTech) adoption on corporate loan spreads and lending practices.Design/methodology/approachThis study examines the impact of FinTech adoption by banks on corporate loan spreads and lending practices. By analyzing data from bank 10-K filings, we develop a novel metric to assess FinTech adoption at the individual bank level. Our analysis reveals a significant positive correlation between increased FinTech adoption and higher corporate loan spreads, particularly for loans that are relatively informationally opaque. This causality is further validated through a quasi-natural experiment. Additionally, we identify trends toward loans with smaller sizes and longer maturities in banks with advanced FinTech integration.FindingsUsing a sample of corporate loans issued from 1993 to 2020, this paper documents a significant positive relationship between a bank’s increased FinTech adoption and higher loan spreads. This correlation is especially noticeable for loans that are informationally opaque. Moreover, the paper reveals trends toward smaller loan sizes and longer maturities with advanced FinTech integration in banks. Overall, these findings indicate FinTech enhances efficiency in processing hard information and holds the potential to enhance financial inclusion.Originality/valueThis paper contributes to two significant strands of finance literature. First, it highlights how banks with advanced FinTech integration gain advantages through enhanced processing of hard information. Furthermore, it underscores the role of FinTech in promoting financial inclusion, particularly for those borrowers facing informational opacity.

Digital technology and on-farm responses to climate shocks: exploring the relations between producer agency and the security of food production

AbstractRecent research into climate shocks and what this means for the on-farm production of food revealed mixed and unanticipated results. Whilst the research was triggered by a series of catastrophic, climate related disruptions, Australian beef producers interviewed for the study downplayed the immediate and direct impacts of climate shocks. When considering the changing nature of production under shifting climatic conditions, producers offered a commentary on the digital technology and data which interconnected with climate solutions deriving from both on and off the farm. Perceptions of digital technologies were mixed. Some viewpoints outlined how data driven climate solutions supported on farm planning and decision making, helping to manage climate risks and shocks. However, alongside these narratives, concerns were raised about satellite-based sustainability surveillance and their implications for producer agency. These concerns include the data-informed actions of non-farming third parties, such as bank loan call-ins for properties perceived to be a climate risk, remote surveillance of ground cover, and the commercial re-appraisal of pastoral lands as carbon sinks. Digital solutions to climate shocks thus emerge as inherently ambivalent, a response to shocks and a potential catalyst for renewed crisis. Drawing upon the theoretical lens of relationality, we argue that digital data are increasingly entangled with other material and non-material elements that may disrupt and/or reconfigure the management of farming and with that, the future security of food production. In some instances, data-based solutions to climate risks and shocks present even greater risks to producer agency than climate risks and shocks themselves.

Tracks of change: The rise and fall of Bolivia's National Railway Company (ENFE): 1965–1995

This article examines the Bolivian State Railway Company (ENFE) from its inception in 1965 to its privatisation in 1995, focusing on its structural evolution, growth, and modernisation in the context of Bolivian state capitalism. It underscores ENFE's substantial contribution to Bolivia's economy, particularly in freight and passenger transport. By comparing ENFE’s labour productivity and financial ratios with other Latin American state railways, this study reveals ENFE's distinct capability for revenue generation due to limited road competition. Key primary sources include ENFE annual reports, World Bank loan documents, archival correspondence, transport studies, and the local press. This article argues that ENFE successfully addressed transportation challenges and contributed to Bolivian economic development. These findings provide valuable insights into Bolivian economic history and Latin American railway historiography through a case study of a strategic state-owned enterprise, illustrating the potential of leveraging World Bank sources for future comparative research on Latin American railways.

Environmental, Social, and Governance Scores and Loan Composition Inside United States Banks

We analyze the loan portfolios of United States banks from 2013 to 2023, showing that high environmental, social, and governance (ESG) banks have larger shares of consumer loans and commercial loans and smaller shares of construction loans and real estate loans. We also find that the governance pillar (G) is more tightly related to the bank loan composition compared to the environmental (E) and social (S) pillars. Furthermore, we show that construction loans and real estate loans decrease more considerably with bank ESG scores inside countries with high gas emissions, i.e., where ESG issues would arguably be more serious. Our interpretation is that sustainable banks are reluctant in lending money for real estate projects, exposing them to potentially high ESG risk. These findings contribute to developing a deeper insight about the relationship between ESG and bank lending, which, in the previous literature, has been treated more frequently in aggregate terms instead of separating loan types. Our outcomes suggest that sustainability is crucial for the availability of funds in the real estate sector, delivering important insights to bank and real estate managers, besides policy makers.

Diffusing Risk: Bureaucratic Agency, UN Security Council Horse-Trading, and the Role of Co-Financing

Abstract Political lending is problematic for the operations of multilateral development banks (MDBs) since politically motivated aid has a greater default risk than other aid projects. MDB bureaucrats, therefore, face a dilemma. On the one hand, they want to please major shareholders by engaging in political lending. On the other hand, they want to mitigate their MDB's exposure to excessive risk. One way to solve this dilemma is to share the risk of loans with other lenders through co-financing. I expect that as countries’ share of politically motivated aid increases, these countries’ portfolios will receive more co-financed loans. Using newly collected loan-level data from the European Bank for Reconstruction and Development, I find that UNSC membership increases the number of co-financed loans by 38.5 percent and increases the share of co-financed loans in a country's European Bank for Reconstruction and Development loan portfolio by 19.1 percentage points. I demonstrate that these results are consistent with the argument that MDB bureaucrats want to mitigate risk.

The Monetary Theory of Production and the Supermultiplier: What Determines Savings?

ABSTRACT The paper presents a closed-economy stock-flow consistent model aimed at outlining the natural synthesis between two fundamental pillars of the post-Keynesian approach: the supermultiplier (SM) model and the monetary theory of production based on the notion of endogenous money. Such integration allows the defining of the whole economic mechanism describing the financing of autonomous components and investments, as well as the determination of output and saving within a monetary economy of production. Our contribution argues that the endogenous money theory plays a non-ancillary role within the SM approach. By comparing different scenarios describing the financing of autonomous components of demand and investments (bank loans, retained profits, CB, and equity emissions), we point out that the endogenous process of money creation is the only structural mechanism through which the autonomous components can exist and be independent of current income, and Keynesian causality materializes in the long-run. In general, it is the initial finance of investments and autonomous components through endogenous money that allows them to be ex-post founded by household saving. Conversely, the initial finance through the existing stock of wealth, although it does not intact the investments-saving causality, can be considered only a temporary spin-off of the endogenous money process.

The Potential of AI in Performing Financial Sentiment Analysis for Predicting Entrepreneur Survival

The aim of this study is to investigate the potential of ChatGPT in analyzing the financial sentiment analysis of entrepreneurs. Sentiment analysis involves detecting if it is positive, negative, or neutral from a text. We examine several prompts on ChatGPT-4, ChatGPT-4.0, and LeChat-Mistral and compare the results with FinBERT. Then, we examine the correlation between scores given by both tools with the type, size, and age of the company. The results have shown that scores given by FinBERT are mostly significant and positively correlated with sustainable variables. By sharing these results, we hope to stimulate future research and advances in the field of financial services, particularly bank loans.

Digital credit for all? An empirical analysis of mobile loans for financial inclusion in Kenya

ABSTRACT Digital credit has shown considerable success in Kenya and is expected to provide opportunities for individuals excluded from formal loans because of their lower socioeconomic status. However, evidence supporting the greater positive impact of digital credit on financial inclusion than that of traditional loans is lacking. This study employs a multinomial logistic regression to investigate the impact of digital credit services, particularly mobile banking and FinTech loans (MBLs and FTLs), on financial inclusion in Kenya. MBLs appear to be less accessible to vulnerable populations, such as women, low-educated groups, and casual workers although both MBLs and FTLs are equally accessible to rural populations. In contrast, FTL services show no such constraints in terms of accessibility for women and low-income workers. This disparity indicates the relative inaccessibility of MBLs to vulnerable groups compared to that of FTLs; however, note that greater loan access does not always benefit vulnerable groups.

CBDC and banking stability: Modeling cascading effects on reserves, lending, and liquidity

This research uses a dynamic modelling approach to design and simulate an equilibrium model of the interaction between CBDC issuance, deposits and bank reserves. With many central banks announcing their intention to issue central bank digital currencies (CBDCs), it has become imperative to rigorously analyse the potential impact of these issues on the banking system. This study aims to examine the implications of the introduction of CBDCs within a robust analytical framework, in order to inform policymakers and financial sector players about the possible consequences of this major monetary innovation. The study results show that overconfidence of economic agents towards CBDCs can drastically reduce bank reserves, thereby limiting the lending capacity of banks and creating liquidity problems. Similarly, increasing reserve requirements in fiat currency for each unit of CBDC issued can constrain bank reserves and restrict loans and deposits. Additionally, a rise in interest rates on CBDC-related loans can discourage borrowers, thereby reducing loan demand and affecting banking activity. An increase in interest rates leads to a decrease in the quantity of CBDC in circulation, an increase in bank reserves and deposits in CBDC, and a decrease in bank loans. Monitoring the level of confidence of economic agents towards CBDCs is crucial to avoid excessive speculation.

LIBOR Discontinuation and the Cost of Bank Loans

LIBOR Discontinuation and the Cost of Bank Loans

Bank loan agreement in the conditions of digitalization

Bank loan agreement in the conditions of digitalization

Environmental performance shortfalls and corporate green innovation: The role of green credit regulation

This paper investigates the effects of environmental performance shortfalls on corporate green innovation. On the basis of the theoretical framework of environmental performance feedback and using a sample of eight major energy-consuming industries in China, a negative relationship is confirmed that environmental performance below but in the neighborhood of peer level makes firms more inclined to pursue green innovation. Higher risk-taking willingness for firms with environmental performance below but close to peer level strengthens innovative intention. This correlation is pronounced when corporate financial performance, green innovation performance, and executive compensation are below peer level. We also find that green credit constrained firms prefer to engage in innovation activities when their environmental performance is below but near peer level and they have fewer bank loans simultaneously. The findings offer insights into the innovation behavior of firms in response to environmental underperformance.

Asset Allocation and Bank Resilience: China Insights

This research is based on the panel data from the annual reports of Chinese commercial banks for the years 2010-2020. A panel data model is constructed to empirically investigate the influence of commercial banks' asset allocation on financial stability. The study innovatively explores the differences between large-scale banks and general-scale banks in this context, as well as the interaction effects between large-scale banks and their methods of capital application. The findings indicate that: firstly, the higher the proportion of corporate loans in commercial banks, the stronger the stability. With the support of national policies, the operating environment for emerging industries has improved, leading to a reduction in bankruptcy risks and further decreasing the credit risk of related banks, which contributes to the enhancement of banks' financial stability. Additionally, compared to securities investments affected by market fluctuations, corporate loans are less susceptible to significant capital changes, and their risks are primarily determined by the operating conditions of the related enterprises, making them relatively controllable. Secondly, the higher the proportion of personal loans in commercial banks, the stronger the stability. The largest part of personal loans consists of housing mortgage loans, which have a low non-performing loan rate and high quality in China. Therefore, an increase in personal loans helps to enhance the financial stability of banks. Thirdly, large-scale banks exhibit lower financial stability. Due to the redundancy in management personnel in large-scale banks, management costs, adjustment costs, and transaction costs are relatively high. Furthermore, it is worth noting that because large-scale banks hold a significant amount of surplus funds, they are prone to excessive securities investments. Fluctuations in the securities market increase external risks and decrease financial stability.

Credit card default prediction using ML and DL techniques

The banking sector is widely acknowledged for its intrinsic unpredictability and susceptibility to risk. Bank loans have emerged as one of the most recent services offered over the past several decades. Banks typically serve as intermediaries for loans, investments, short-term loans, and other types of credit. The usage of credit cards is experiencing a steady increase, thereby leading to a rise in the default rate that banks encounter. Although there has been much research investigating the efficacy of conventional Machine Learning (ML) models, there has been relatively less emphasis on Deep Learning (DL) techniques. The application of DL approaches to credit card default prediction has not been extensively researched despite their considerable potential in numerous fields. Moreover, the current literature frequently lacks particular information regarding the DL structures, hyperparameters, and optimization techniques employed. To predict credit card default, this study evaluates the efficacy of a DL model and compares it to other ML models, such as Decision Tree (DT) and Adaboost. The objective of this research is to identify the specific DL parameters that contribute to the observed enhancements in the accuracy of credit card default prediction. This research makes use of the UCI ML repository to access the credit card defaulted customer dataset. Subsequently, various techniques are employed to preprocess the unprocessed data and visually present the outcomes through the use of exploratory data analysis (EDA). Furthermore, the algorithms are hypertuned to evaluate the enhancement in prediction. We used standard evaluation metrics to evaluate all the models. The evaluation indicates that the AdaBoost and DT exhibit the highest accuracy rate of 82 ​% in predicting credit card default, surpassing the accuracy of the ANN model, which is 78 ​%.