Credit Decisions Research Articles

The role of loan loss provisions in income inequality: Evidence from a sample of banking institutions

This study explores the empirical link between income inequality and banks’ Loan Loss Provisions (LLPs) through a sample of banking institutions from 132 countries, applying a panel regression methodology. The evidence reveals that higher LLPs have a positive impact on income inequality, and the findings remain valid across various model specifications and income inequality measures. The results also hold against various robustness tests, such as different bank sizes, developed vs. emerging countries, the impact of the 2008 global financial crisis, and the controlling for risk. The implications are relevant for stakeholders, including regulators that endeavor to protect banking systems against expected and unexpected losses via LLPs. Specifically, since credit decisions have substantial effects on income inequality, regulators should mitigate the accumulation of LLPs, allowing more funds to be available for other banking system activities and functions.

The Effect Of Financial Literacy And Income On Credit Taking Decisions With Financial Behavior As A Moderating Variable In Pontianak City MSMES

This study aims to examine how financial behavior affects the relationship between financial literacy, income, and lending decisions. This research was conducted on MSMEs in Pontianak City that were officially registered with the Pontianak City Office of Cooperatives, Micro, and Small Enterprises in 2023. The research method used was quantitative with descriptive analysis. To select the research sample, a purposive selection strategy was used with a total sample size of 150 MSMEs. Moderated Regression Analysis was used to test the moderating effect of financial behavior on the correlation between financial literacy, income, and loan decisions. The findings show that financial literacy has a favorable and large impact on credit decisions, while income has an adverse and large impact. In addition, financial literacy and income play an important role in determining lending decisions. The findings of the moderation test using MRA show that financial behavior can moderate the relationship between financial literacy and income on lending decisions.

Using Grocery Data for Credit Decisions

Many consumers across the world struggle to gain access to credit because of their lack of credit scores. This paper explores the potential of a new alternative data source, grocery transaction data, for evaluating consumers’ creditworthiness. Our analysis takes advantage of a unique, individual-level match of credit card data and supermarket loyalty card data. By developing credit scoring algorithms that either exclude or include grocery data, we illustrate both the incremental value of grocery data for credit decisions and its boundary conditions. We demonstrate that signals from grocery data can improve credit approval decisions, particularly for individuals who lack traditional credit scores. Furthermore, as a consumer establishes a relationship with lenders and builds a credit history, the marginal value of incorporating grocery data diminishes. These findings highlight the potential of grocery data in informing credit decisions and, consequently, in enabling financial institutions to extend credit to consumers who lack traditional credit scores. This paper was accepted by David Simchi-Levi, marketing. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.02364 .

INFORMATION AS A CONSUMER PROTECTION INSTRUMENT ON THE CONSUMER CREDIT MARKET

The aim of the article was to assess the opinions of consumer credit borrowers on the information message provided to them by lenders at the pre-contractual stage. The opinions were presented in the context of the demographic characteristics of the respondents. The research question posed was whether, taking into account demographic characteristics, the assessment of consumer credit borrowers regarding the information message obtained at the pre-contractual stage varied. The research methodology included a questionnaire and Computer-Assisted Telephone Interviewing (CATI). For the information analysis, we employed the following non-parametric tests: the chi-square test of independence, the Mann–Whitney U test and the Kruskal–Wallis H test. No significant differences were observed in all the parameters of the assessment of the information message, which means that the answer to the research question was negative. The findings suggest that even though borrowers may positively assess the compliance with the information obligations by lenders, it does not mean that all credit decisions made were rational and appropriate to their financial situation. Our study focused on the consumer protection and employed non-parametric tests to analyze the consumer credit borrowers’ assessment of the information provided to them by lenders.

Financial Literacy on Banking Sector Credit Usage: Evidence from Sri Lanka

This study uses quantitative analysis to investigate the relationship between financial literacy and credit usage in the banking sector in Sri Lanka. A sample of 210 respondents from various regions, including the rural Welioya and urban Colombo Fort, was selected to provide diverse perspectives on financial behaviours. Financial literacy, the independent variable, was assessed through respondents' knowledge in critical areas: interest compounding, inflation, time value of money, returns, and risk management. These components represent the fundamental financial understanding necessary for informed credit decisions. The dependent variable, banking sector credit usage, included various forms of credit such as personal loans, credit cards, and mortgages. Data were collected through structured interviews and questionnaires, comprehensively capturing respondents' financial knowledge and credit usage behaviours. The data were analyzed using SPSS software, facilitating robust statistical analysis. Descriptive statistics showed satisfactory financial literacy and banking sector credit usage levels, indicating basic financial proficiency and active engagement with banking credit facilities. Further inferential analysis using correlation and regression tests revealed a significant positive correlation between financial literacy and banking sector credit usage. Individuals with higher financial literacy were likelier to utilize banking credit effectively and responsibly. These findings underscore the critical role of financial literacy in enhancing banking sector credit usage. Improved financial literacy leads to better understanding and management of credit, contributing to more stable personal finances and overall economic well-being. For policymakers and educators in Sri Lanka, these insights highlight the importance of financial education initiatives. By investing in programs that boost financial literacy, it is possible to foster more informed and prudent credit usage, ultimately contributing to the stability and growth of the national economy.

THE ROLE OF ARTIFICIAL INTELLIGENCE IN IMPROVING MICROFINANCE PRODUCTIVITY

More than two billion people do not have access to banking institutions, this is where the important role of microfinance distributes small loans to the poor so that they can access the financial industry. This study aimsto determine the role of artificial intelligence in improving the productivity of microfinance institutions. The qualitative research was employed with a library study approach by reviewing 25 selected journals indexed by Scopus and supported by Rank 1 and Rank 2 Science and Technology Index journals, with research published in 2000 – 2022. A sample of papers based on keywords in the publication was analyzed using bibliometric with VOSviewer application. The research results stated that microfinance institutions were established to provide benefits to poor and lowincome communities. The role of artificial intelligence can increase the productivity of the financial industry. Artificial intelligence can be used to analyze the feasibility and risk of default for customers and potential customers. The advantages of artificial intelligence include speed of decision-making, higher levels of automation for credit decisions, and the ability to be used remotely. In addition, artificial intelligence can also be used to generate investment signals that grow exponentially and generate data for future analysis.

The Influence Of Collateral And Loan Term On Credit Decisions At The Swadharma Savings And Loans Cooperative Artha Gemilang In Blahbatuh District, Gianyar Regency

There is attention and problems encountered related to collateral and loan terms in credit decisions at the Swadharma Artha Gemilang Savings and Loans Cooperative in Blahbatuh District, Gianyar Regency. The aim of the research is to determine the effect of collateral and loan term simultaneously and partially on credit decisions at the Swadharma Artha Gemilang Du Savings and Loans Cooperative, Blahbatuh District, Gianyar Regency. The number of samples was determined using the Accidental Sampling method as many as 90 credit customers at the Swadharma Artha Gemilang Savings and Loans Cooperative in Blahbatuh District, Gianyar Regency. Data were analyzed using validity and reliability tests, classical assumption tests, multiple linear regression analysis, determination analysis, statistical analysis of the t test (t-test) and statistical analysis of the F test (F-test). The results of the research show that there is a positive and significant influence, both partially and simultaneously, between collateral and loan term on credit decisions at the Swadharma Artha Gemilang Savings and Loans Cooperative in Blahbatuh District, Gianyar Regency. Keywords: ,

The Machine Learning Methods For Micro-Credit Scoring: The Case Of Micro-Financing In Mongolia

As a result of growing digital technologies in the financial sector, the traditional slow lending process is being replaced by fast and easy digital lending systems that can make decisions in real time. Both lenders and borrowers have experienced the benefits of digital lending, the activities of microfinance institutions have expanded rapidly and the volume of digital microloans has increased significantly worldwide, including Mongolia. At the same time with the growing volume of digital microloans in Mongolia, the rationality of credit risk management has been becoming more critical. Credit quality is the most important factor in optimal credit risk management. It depends on determining the customer's creditworthiness and making accurate credit decisions. This research focuses on a credit scoring system to improve the digital loan evaluation system of the Mongolian microfinance institute. This study aims to contribute to the development of possible credit scoring systems for Mongolian microfinance institutions by comparing several machine-learning approaches based on loan datasets of a non-banking microfinance institute in Mongolia. The result shows the ensemble methods Random Forest and XGBoost Tree's accuracies are higher than other machine learning models for the microloan borrowers' repayment status prediction.

Analisis Sistem Penyaluran Kredit Perbankan (Studi Kasus Pada PT BPR Supra Artapersada Kantor Pusat Operasional)

The banking industry has a role to support the economy which can be done through collecting funds and channeling it back in the form of credit, so that the bank is tasked with being a financial intermediary that connects existing funds from economic units with excess funds to economic units that need financial assistance (deficit), whether it's for working capital, investment and consumption (Effendi 2020). This study aims to find out whether the credit distribution system implemented by PT BPR Supra Artapersada at the Operational Head Office is based on procedures. Use of Qualitative Description as a method in this study, the data used is by collecting data from research objects and analyzing procedures and credit distribution systems. The research data was collected through interviews, observation, and documentation. The results of the research show that credit distribution processes are carried out in various stages of the procedure, namely: the credit application stage, the interview stage, data analysis, debtor visits, collateral assessment, credit analysis, credit decisions, credit binding, and credit disbursement. By having an in-depth understanding of this process and the factors that influence it, we can identify areas for improvement and implement relevant measures to enhance the success of the credit distribution system.

Motivation's Influence on Credit Decisions: Unveiling the Moderating Effect of Brand Awareness in PT Bank Syariah Indonesia Tebing Tinggi Branch

The number of MSMEs in Indonesia is very large, but the portion of credit financing to MSMEs is still very small when compared to the percentage of the number of MSMEs. Therefore, this study aims to analyze the effect of MSME customer motivation on credit decisions at PT Bank Syariah Indonesia through brand awareness as a moderating variable. This research is a quantitative descriptive study conducted on 250 MSME customers of PT Bank Syariah Indonesia, who have credit with current status and are willing to be respondents. Sampling method with accidental sampling. This study uses primary data collected by questionnaires distributed to respondents. Respondents' perceptions were measured using a Likert scale. Research with SEM model, data processing with PLS 3.0 smart software. The results showed that motivation and brand awareness has a significant effect on MSME customer credit decisions at PT Bank Syariah Indonesia Tebing Tinggi branch. Brand awareness can be a moderating variable in the influence of motivation on credit decisions of MSME customers of PT Bank Syariah Indonesia Tebing Tinggi Branch. The implication of this research is targeting the policy of increasing Islamic bank credit financing that is oriented towards brand awareness.

Multi-attribute decision making method based on the generalised spherical fuzzy Heronian operators

Traditional fuzzy decision-making methods still have certain limitations in practical applications, such as the problem of the sum of attribute memberships and non-memberships possibly exceeding 1. Additionally, due to the attributes in real decision-making processes often not being mutually independent but rather exhibiting a certain degree of correlation, traditional fuzzy decision-making methods may not fully capture and express this complexity. To overcome these limitations, this paper proposes a new multi-attribute decision-making method addressing the problem of integrating information with correlated attributes in the generalized spherical fuzzy environment. Initially, by combining the generalized spherical fuzzy set with the Heronian averaging operator, the paper introduces the generalized spherical fuzzy weighted Heronian averaging operator and thoroughly discusses some valuable properties of both operators, providing corresponding proofs. Furthermore, the paper proposes the multi-attribute decision-making method using the generalized spherical fuzzy weighted Heronian averaging operator, enriching not only the theoretical framework of multi-attribute decision-making methods but also offering more possibilities for practical applications. Finally, the application of this method in the field of commercial bank lending decision-making will be further explored to enhance the accuracy and efficiency of credit decisions, reduce risks, and promote the healthy development of the banking industry.

Sistem Akuntansi Pemberian Kredit Agunan Rumah Dalam Mendukung Pengendalian Internal Pemberian Kredit

This research aims to explain and analyze how the Accounting System for Providing Home Collateral Credit at PT. Bank Tabungan Negara, Tbk Gorontalo Branch Office. The method used in the research is a qualitative descriptive review. PT. Bank Tabungan Negara, Tbk Gorontalo Branch Office has implemented an accounting system for granting home mortgage credit well and meets the internal control elements and is in accordance with established standards, and is supported by an E-Loan system which can provide convenience in the credit granting process and more effective and efficient credit decision making from the credit granting system.

Predicting Bank Loan Eligibility Using Machine Learning

With the increasing needs of people, the demand for bank loans also rises. Each day, banks receive numerous loan applications from various customers and individuals, however, not all applicants are approved. Banks typically assess an applicant's eligibility before processing a loan application, a process that can be time-consuming and complex. In evaluating loan applications and making credit decisions, banks often rely on their credit scoring and risk assessment systems. Nevertheless, there are still cases where some applicants default on their payments annually, resulting in significant financial losses for financial institutions. In this research, we utilized machine learning algorithms to analyze a dataset of approved loans and predict which applicants are most deserving of a loan. The study incorporates customers' historical data, including factors such as age, income type, loan annuity, last credit bureau report, organization type, and length of employment. Various machine learning methods like Random Forest, XGBoost, Adaboost, Lightgbm, Decision tree, and K-Nearest Neighbor were utilized to identify the most influential features that impact prediction outcomes. These algorithms were then compared using standard metrics, with Logistic Regression achieving the highest accuracy rate of 92%. Key Words: Bank Loans, Logistic Regression, Credit Report, Risk Assessment

Innovative Application of Artificial Intelligence Technology in Bank Credit Risk Management

With the rapid growth of technology, especially the widespread application of artificial intelligence (AI) technology, the risk management level of commercial banks is constantly reaching new heights. In the current wave of digitalization, AI has become a key driving force for the strategic transformation of financial institutions, especially the banking industry. For commercial banks, the stability and safety of asset quality are crucial, which directly relates to the long-term stable growth of the bank. Among them, credit risk management is particularly core because it involves the flow of a large amount of funds and the accuracy of credit decisions. Therefore, establishing a scientific and effective credit risk decision-making mechanism is of great strategic significance for commercial banks. In this context, the innovative application of AI technology has brought revolutionary changes to bank credit risk management. Through deep learning and big data analysis, AI can accurately evaluate the credit status of borrowers, timely identify potential risks, and provide banks with more accurate and comprehensive credit decision support. At the same time, AI can also achieve real-time monitoring and early warning, helping banks intervene before risks occur and reduce losses.

Review of the Impact of Corporate CSRD on the Capital Cost of Debt

With the expansion of credit scale, green credit releases a large number of ecological dividends. CSR reporting is different from tightly regulated financial reporting and there are no uniform standard to regulate and monitor its disclosure. At the same time, because CSR reports mainly contain non-financial information of text, and text is more flexible and easier to manipulate than numbers, compared with financial reports, CSR reports have a large space for subjective manipulation, which runs counter to the original intention of reducing information asymmetry. Existing studies have confirmed that CSR intonation can provide incremental information to external information users, so it is worth further exploring whether creditors will obtain information from CSR reports when making credit decisions. Therefore, this paper tries to take CSR intonation as the entry point, summarize the motivation and consequences of CSRD, the relationship between social responsibility and debt capital cost, discuss the impact of CSRD behavior on the financing ability of enterprises, further improve the theoretical basis and conclusion of CSRD and enterprise financing ability, and promote the sustainable development of enterprises.

Exploration of Commercial Banks’ Credit Business in the Context of Big Data

With the development of big data technology, there are many problems in the credit system of commercial banks, commercial banks continue to improve the credit system to better ensure the good development of loan business. Micro, Small, and Medium Enterprises (Msmes) are the foundation of national economy, it is very important to study the financing of msmes, commercial banks should make correct credit decision for different msmes. In this paper, we solve the problem of commercial bank making loan decision to loan enterprise from the source, and study the efficient decision-making method. Based on RFM model, we propose a new YNA model to determine the main value of the studied credit customers, k-means clustering algorithm is used to determine the stratification of certain type of enterprises, KNN model and logistic regression model is used to help banks to identify the risk of msmes. It is found that the use of big data technology to facilitate bank credit decision-making improves the efficiency and accuracy of commercial bank credit business review of different enterprises. Based on the essence of commercial bank credit business model, this paper combines the characteristics of commercial bank credit business and big data through the financing of small and medium-sized enterprises.

Corporate External Guarantees and M&A Performance Commitments

This study analyzes the effect of corporate external guarantees on M&A performance commitments using a research sample of Chinese A-share listed companies from 2008 to 2022. The study finds that listed companies are more likely to sign performance commitments in M&As and sign higher performance commitments when the proportion of corporate external guarantees is higher. It has been found that corporate external guarantees have a hollowing-out effect compared to the governance effect of corporate guarantees. The higher the proportion of external guarantees, the more insider self-interest behavior exacerbates the financing constraint problem of the enterprise. To alleviate financing constraints, controlling shareholders have a stronger incentive for market value management by signing performance commitments. The analysis of heterogeneity reveals that when management shareholding is low and the two rights of the company are highly separated, the proportion of corporate external guarantees has a more significant effect on M&A performance commitments. This study's findings have reference value for financial institutions when making credit decisions and for regulators to improve the efficiency of M&A supervision.

Retraction Note: COVID-19 Forecast and Bank Credit Decision Model Based on BiLSTM-Attention Network

Retraction Note: COVID-19 Forecast and Bank Credit Decision Model Based on BiLSTM-Attention Network

Monetary Policy and Home Buying Inequality

Abstract Does monetary policy inuence who becomes a home owner? Lower-income home buyers may be more sensitive to interest rates, at least in part because they more frequently come up against binding payment-to-income ratio constraints in credit decisions. Exploiting the timing of high-frequency observations of individual mortgage rate locks around monetary policy shocks, I find that a 1 percentage point policy-induced increase in mortgage rates lowers the presence of lower-income households in the population of home buyers by 1 to 2 percentage points immediately following the shock. Effects are substantially stronger among first-time home buyers, and persist for approximately one year.

Robust integration of blockchain and explainable federated learning for automated credit scoring

This article examines the integration of blockchain, eXplainable Artificial Intelligence (XAI), especially in the context of federated learning, for credit scoring in financial sectors to improve the credit assessment process. Research shows that integration of these cutting-edge technologies is in its infancy, specifically in the areas of embracing broader data, model verification, behavioural reliability and model explainability for intelligent credit assessment. The conventional credit risk assessment process utilises historical application data. However, reliable and dynamic transactional customer data are necessary for robust credit risk evaluation in practice. Therefore, this research proposes a framework for integrating blockchain and XAI to enable automated credit decisions. The main focus is on effectively integrating multi-party, privacy-preserving decentralised learning models with blockchain technology to provide reliability, transparency, and explainability. The proposed framework can be a foundation for integrating technological solutions while ensuring model verification, behavioural reliability, and model explainability for intelligent credit assessment.