Loan Applications Research Articles

Social Penetration Analysis in Self-Disclosure of Online Loan Application Users to Their Partners

Abstract: This study aims to analyze the disclosure of online loan users to their partners through social penetration theory. Information technology-based money lending services are one of the innovations in the financial sector, utilizing technology to facilitate lending transactions without meeting in person. This transaction mechanism is carried out through a system provided by the Fintech Lending Organizer, either through an application or a website. Several problems arise because of the characteristics of online loans that often terrorize and intimidate people close to them without asking permission. This study is descriptive, using data collection techniques on ten pairs of online loan informants with their partners. Data analysis was carried out using interactive techniques involving interactions between data collection, data reduction, data presentation, and conclusions. Based on the social penetration analysis, five pairs of informants decided to separate/divorce, while five pairs did not divorce. The five pairs of informants who did not divorce were at stages 3 (affective exchange) and 4 (stable exchange). The five pairs who divorced were at stage 2 (exploration of affective exchange), which decreased the development of their relationship to deterioration. Couples who eventually divorce receive negative Feedback from their partners in the form of anger, annoyance, and regret. The perpetrators decide to get involved in online loans without discussing it with their partners but also have no desire to discuss it. The perpetrators' mindset, household economic problems, characteristics of online loans, and skills in communicating issues to their partners color the couple's interactions towards the end of the relationship. On the other hand, supportive Feedback, personality, and gender of the partner are factors that influence self-disclosure.

Financial Literacy Program in Introducing Online Loan Applications to University Students

The Covid-19 pandemic in 2021 impacted the increasing diversity of basic needs in society and accelerated digitalization, which has now become a lifestyle. Easy access to digital platforms for shopping, education, investment, payments, and loans has made wise financial management essential. According to data from the Financial Services Authority (OJK), the total distribution of online loans from Fintech Lending in Indonesia reached IDR 225.55 trillion in 2022, an increase of 44.6% from the previous year. Fintech simplifies loan access through peer-to-peer (P2P) lending products that are fast and without complicated requirements, attracting the interest of young generations. Therefore, financial literacy is crucial for Sampoerna University students to address the low level of financial understanding amid the proliferation of online loan applications. Knowledge of risk management is needed so that students can make wise and responsible financial decisions and manage their finances effectively.

Environmental Factors Affecting Growth of MSME’s Owned by Entrepreneurs with Disabilities in Rural Areas in Kenya: A Case of Meru County

Purpose: In the recent past, the government of Kenya has paid much attention to entrepreneurship primarily because of the positive relationship between entrepreneurship, job creation and economic growth. In Kenya, over 1.3 million Persons live with disabilities with most of them being generally economically deprived and over-represented among the poor. The aim of the study study was to investigate the external business factors affecting growth of MSME’s owned by entrepreneurs with disabilities in rural areas in Kenya: a case of Meru County. Methodology: The target population comprised of 2021 entrepreneurs with disabilities. The researcher used a combination of stratified sampling, census and purposive sampling method to select the respondents. Data was collected by administration of questionnaires designed based on the research objectives. The data collected was then coded, entered, and analyzed using Statistical Package for Social Sciences (SPSS). Findings: The findings showed that if all factors; political-legal environment, social –cultural environment and technological environment had a positive and significant effect on growth of MSME’s. Political –legal factors had the greatest effect at 0,882, followed technological at 0.799 while social factors had 0.633. Unique Contribution to Theory, Practice and Policy: The study recommended that the government should provide adequate and timely funding to vocational and technical training institutions. Government funding programs such as Women, Youth and Uwezo fund should increase the minimum loan amount, simplify loan application process and ensure timely disbursement of funds as well as establish strategies of lending to individuals. Finally, a mentorship office should be established in every constituency to offer sector specific programs including activities geared towards exposing EwD’s to successful entrepreneurs.

Effective Machine Learning Techniques for Dealing with Poor Credit Data

Credit risk is a crucial component of daily financial services operations; it measures the likelihood that a borrower will default on a loan, incurring an economic loss. By analysing historical data for assessment of the creditworthiness of a borrower, lenders can reduce credit risk. Data are vital at the core of the credit decision-making processes. Decision-making depends heavily on accurate, complete data, and failure to harness high-quality data would impact credit lenders when assessing the loan applicants’ risk profiles. In this paper, an empirical comparison of the robustness of seven machine learning algorithms to credit risk, namely support vector machines (SVMs), naïve base, decision trees (DT), random forest (RF), gradient boosting (GB), K-nearest neighbour (K-NN), and logistic regression (LR), is carried out using the Lending Club credit data from Kaggle. This task uses seven performance measures, including the F1 Score (recall, accuracy, and precision), ROC-AUC, and HL and MCC metrics. Then, the harnessing of generative adversarial networks (GANs) simulation to enhance the robustness of the single machine learning classifiers for predicting credit risk is proposed. The results show that when GANs imputation is incorporated, the decision tree is the best-performing classifier with an accuracy rate of 93.01%, followed by random forest (92.92%), gradient boosting (92.33%), support vector machine (90.83%), logistic regression (90.76%), and naïve Bayes (89.29%), respectively. The classifier is the worst-performing method with a k-NN (88.68%) accuracy rate. Subsequently, when GANs are optimised, the accuracy rate of the naïve Bayes classifier improves significantly to (90%) accuracy rate. Additionally, the average error rate for these classifiers is over 9%, which implies that the estimates are not far from the actual values. In summary, most individual classifiers are more robust to missing data when GANs are used as an imputation technique. The differences in performance of all seven machine learning algorithms are significant at the 95% level.

Personal Bankruptcy Prediction Using Logistic Regression Model

According to the Insolvency Department of Malaysia, as of December 2023, 233,483 Malaysians are currently involved in bankruptcy cases due to their defaults on hire purchase loans, credit card loans, personal loans, housing loans, and business loans. This is indeed a critical issue because the growing number of personal bankruptcy cases will hurt the Malaysian economy as well as society. From an individual's economic perspective, bankruptcy minimizes their chances of getting a job. Apart from that, their accounts will be frozen, they will lose control of their properties and assets, and they will not be allowed to start any business or be a part of any company's Board of Directors. Bankrupts also will be rejected from any loan application. This paper examines this problem by developing a personal bankruptcy prediction model using the logistic regression technique. This paper defines "bankrupt" as terminated members who failed to settle their loans. The sample comprised 24,546 cases with 17% settled cases and 83% terminated cases. The data included a dependent variable, i.e., bankruptcy status (Y=1(bankrupt), Y=0(non-bankrupt)), and 12 predictors. Upon completion, this paper succeeds in coming out with a reliable personal bankruptcy prediction model and significant variables of personal bankruptcy. The findings of this paper are very beneficial and significant to creditors, banks, the Malaysia Department of Insolvency, potential borrowers, members of AKPK, and society in general in raising awareness of personal bankruptcy risks and such information may help them to take preventive measures in minimizing the number of personal bankruptcy cases.

Access to finance and its impact on operational efficiency of MSMEs: mediating role of entrepreneurial personality and self-efficacy

PurposeThe present study aims to investigate the factors of loan access that affect entrepreneurial self-efficacy (ESE) and operating efficiency of Indian Micro, Small and Medium Enterprises (MSMEs). Furthermore, the study intended to investigate the influence of ESE on the operating efficiency of Indian MSMEs and its mediating role.Design/methodology/approachIn this study, exploratory research design is used. The study heavily relies on the primary data which has been collected by using the survey research method from a cross-section of 617 women-owned MSMEs, located in urban, rural, suburban and exurban areas of Haryana, Uttarakhand, Himachal Pradesh and NCR-Delhi. The partial least square structural equation modeling method version 3.3.3 has been used to evaluate.FindingsIn terms of the selected factors affecting access to finance, it has been established that the Loan Formalities, Banking Process, Loan Process, Staff Responsiveness and Incentive Scheme have a positive and significant influence in enhancing accessibility to finance and improving the self-efficacy and operating performance of firms. The findings also show that ESE mediates the relationship between various factors of loan access and the operating efficiency of MSMEs.Research limitations/implicationsThe study’s findings show that entrepreneurial capacity is significantly and favorably impacted by attitudes toward entrepreneurship, ESE, perceived access to findings and business operations. It has also been demonstrated that entrepreneurial intentions are strongly and favorably influenced by entrepreneurial ability to access commercial bank financing for small businesses and the impact of the same on the women-owned MSMEs in India. It also revealed unfavorable loan terms, limited collateral, fear of repaying of loan and intricate loan application were among the many reasons for loan denial.Originality/valueThe study offers a comprehensive approach that simultaneously considers financial accessibility and ESE. This all-encompassing method offers a thorough grasp of the variables affecting MSMEs' operational efficiency (OE). In contrast to earlier research that might have concentrated only on direct relationships, this study explores the mediating mechanisms involved. This study examines how ESE modulates the influence of financing availability on OE, providing a comprehensive understanding of the underlying mechanisms. By taking into account particular MSME sector characteristics like size, industry or regional variations, the study may provide a unique contextual lens. Understanding how these contextual factors interact with entrepreneurial attributes and access to finance adds depth to the analysis.

PPDNN-CRP: privacy-preserving deep neural network processing for credit risk prediction in cloud: a homomorphic encryption-based approach

This study proposes a Privacy-Preserving Deep Neural Network for Credit Risk Prediction (PPDNN-CRP) framework that leverages homomorphic encryption (HE) to ensure data privacy throughout the credit risk prediction process. The PPDNN-CRP framework employs the Paillier homomorphic encryption scheme to secure sensitive loan application data during both the training and inference phases. Implemented using TensorFlow for deep neural network operations and TenSEAL for HE, the framework uses the Kaggle loan dataset to evaluate its performance. The results show that PPDNN-CRP achieved an accuracy of 80.48%, demonstrating competitive performance compared to Privacy-Preserving Logistic Regression (PPLR) at 77.23% and a slight decrease from the non-private DNN-CRP model at 86.18%. The model exhibited strong metrics with a precision of 0.84, recall of 0.91, F1-score of 0.87, and an AUC of 0.74. Security analysis confirms that PPDNN-CRP effectively defends against various privacy attacks, including poisoning, evasion, membership inference, model inversion, and model extraction, through robust encryption techniques and privacy measures. This framework offers a promising approach for achieving high-quality credit risk prediction while maintaining data privacy and complying with legal and ethical standards.

Imperatives of Small-Scale Businesses and Employment Generation in Uyo Local Government Area, Akwa Ibom state

The study investigated the relationship between small scale enterprises and employments generation in Uyo Local Government Area, Akwa Ibom State. A cross-sectional survey research method was adopted for the study. A sample of 361 respondents was drawn from the population of 3725 using Taro Yamane’s scientific sampling formula. For the objectives of the study to be achieved, three hypotheses were formulated and tested. The study utilized structured questionnaire as a major instrument for data collection. 361 questionnaire were distributed and 334 were returned in useable form. Data collected were analyzed using simple percentage and regression. The results of the regression analysis indicated positive relationship between the variables of small scale enterprises and employment generation in Uyo, Akwa Ibom State. It was recommended that; there should be simplified loan application processes and reduced collateral requirements for small scale enterprises to encourage access to funding, there should be government policies that promote and ensure regular maintenance of existing infrastructure to prevent deterioration and reduce disruptions to business operations, and there should be development of entrepreneurship and business management training programs tailored for small scale enterprise owners, covering essential skills like book-keeping, marketing and customer service.

A submodular optimization approach to trustworthy loan approval automation

AbstractIn the field of finance, the underwriting process is an essential step in evaluating every loan application. During this stage, the borrowers' creditworthiness and ability to repay the loan are assessed to ultimately decide whether to approve the loan application. One of the core components of underwriting is credit scoring, in which the probability of default is estimated. As such, there has been significant progress in enhancing the predictive accuracy of credit scoring models through the use of machine learning, but there still exists a need to ultimately construct an approval rule that takes into consideration additional criteria beyond the score itself. This construction process is traditionally done manually to ensure that the approval rule remains interpretable to humans. In this paper, we outline an automated system for optimizing a rule‐based system for approving loan applications, which has been deployed at Hyundai Capital Services (HCS). The main challenge lays in creating a high‐quality rule base that is simultaneously simple enough to be interpretable by risk analysts as well as customers, since the approval decision should be easily understandable. We addressed this challenge through principled submodular optimization. The deployment of our system has led to a 14% annual growth in the volume of loan services at HCS, while maintaining the target bad rate, and has resulted in the approval of customers who might have otherwise been rejected.

Loan Eligibility Prediction Using Machine Learning

Technology has made many improvements, and the banking industry is no exception. Submission of loan applications by people are so many every day, making it more difficult for bank to approve loan. To choose an applicant for loan approval, Banks must consider other bank policies also. Based on a few factors, the bank must choose the proposal that has the best probability of getting granted. It would be time-consuming and unsafe to individually check each applicant before recommending them for loan approval. Based on the prior performance of the person to whom the loan amount was previously accredited, we utilize a machine learning technique in this study to forecast the person who is trustworthy for a loan. This will check the whether the applicant is eligible for the loan or not based upon the any previous loan or running loans whether the applicant is paying back the loan within the deadline or not and it will check many other factors to shortlist the applicant is genuinely eligible for loan or not.

Does Soft Information Mitigate Gender Bias in Corporate Lending?

AbstractGender bias in leadership and decision-making is a well-documented and pervasive topic that continues to garner significant attention in academic research and business literature. In this paper, by exploiting a unique proprietary dataset of 550 mid-corporate loan applications managed by a major European bank, we explore how the use of soft information influences lending decisions of female loan officers as compared to their male counterparts. We find that use of soft information reduces information asymmetry which helps female officers in making diligent lending decisions resulting in increased granted credit with a lower default probability. We also investigate gender affinity within the banking organisation and find that female loan approvers are more likely to be supportive of their subordinate female loan officers by approving more credit to the loan applications handled by female loan officers. Finally, we examine the possible mechanisms that can explain these results, and find that female loan officers are able to better collect and use soft information as they cultivate and maintain deeper firm-bank relationships with their clients due to higher threat of losing or being penalized in their jobs for any possible errors. We also rule out any other possible explanations such as differences in workload, work experience, loan officers’ optimism, managerial ability, and screening capabilities between female and male loan officers. Our findings carry important policy implications, reflected in the optimal allocation of capital in the economy and the reduction of gender-related exclusion, which is vital in creating an equitable society and fostering a more ethical and inclusive workplace.

Impact of Exposure to Online Advertising on The Intention to Download Online Loan Applications Among Young Adults (18-27 Years) in Indonesia

This study examines the impact of online loan advertisements on the behavior of Generation Z (Gen-Z) in Indonesia, specifically their propensity to download loan applications. Data from Gen-Z individuals aged 18-27 were analyzed using Partial Least Squares-Structural Equation Modeling (PLS-SEM) to evaluate the effects of informativeness, entertainment, irritation, and incentives on advertisement value. Findings show that these advertising elements, especially incentives, significantly influence inspire-to behavior. Mediating factors such as advertisement value, perceived risk, and trust also play a significant role. The respondent profile indicates a majority of males, Bachelor's degree holders, and students. The study suggests that enhancing marketing strategies in the online loan sector requires understanding the factors influencing Gen-Z's decisions. It also highlights the importance of reducing unacceptable advertising elements. Future research should consider a more diverse sample and updated theoretical models. This study contributes to digital marketing and consumer behavior literature, providing insights for more effective and responsible online loan advertising in Indonesia.

Character Counts: Psychometric-Based Credit Scoring for Underbanked Consumers

Psychometric-based credit scores measure important personality traits that are characteristic of good borrowers’ behaviors. While such data can potentially improve credit models for underbanked consumers, the utility of psychometric data in consumer lending is still largely understudied. The present study contributes to the literature in this respect, as it is one of the first studies to evaluate the efficacy of psychometric-based credit scores for predicting future loan defaults among underbanked consumers. The results from two culturally diverse samples of loan applicants (Sub-Saharan Africa, n = 1113; Western Europe, n = 1033) found that psychometric scores correlated significantly with future loan defaults (Gini = 0.28–0.31) and were incrementally valid above and beyond the banks’ own credit scorecards. These results highlight the theoretical basis for personality in financial behaviors, as well as the practical utility that psychometric scores can have for credit decisioning in general and the facilitation of financial inclusion for underbanked consumer groups in particular.

The Effect of Student Loan Debt on Emergency Savings and the Moderating Role of Financial Knowledge: Evidence from the U.S. Survey of Household Economics and Decisionmaking

This study examines data from the U.S. 2018 and 2019 Survey of Household Economics and Decision making (SHED) to understand the association between student loan debt and emergency-saving decisions, including the moderating role of financial knowledge. Controlling self-selection bias through a propensity score and coarsened exact matching approach, the findings reveal that individuals with student loan debt are less likely to save for financial emergencies. The findings also show that financial knowledge is positively associated with a higher likelihood of having emergency savings. Furthermore, the results from the moderating analysis indicate a statistically significant interaction effect. Based on the empirical results and the corresponding interaction plots, the findings suggest that targeted financial education may lead to improved financial outcomes for student loan borrowers, rather than assuming that such education occurred prior to a loan application.

Loan application due diligence and financing small firms in Africa

ABSTRACT Banks gauge and verify business potential when processing small business loan applications by conducting due diligence on the firm. Although research has examined the factors that influence small business financing, little attention has been given to understanding the due diligence process that informs credit decision making. We address this gap using signal theory to analyze and develop insights from 24 qualitative interviews. Our study identified 11 key hard and soft signals loan officers use to access small business credit risk during due diligence. The key signals include key person risk, change in leadership risk, articulation of company value, adaptation to change, separating business and personal finances, personal reputation, transparency and willingness to share information, data accuracy and completeness, regular account operation, faulty budget and turnover assumptions, and legitimacy of collateral. The implications for small and medium-sized enterprises finance theory and practice are discussed.

Tinjauan Yuridis Terhadap Shopee Pinjam Pada Marketplace Berdasarkan Ekonomi Islam

Shopee borrows from the marketplace, which is one of the products offered by Shopee to give to users so they can get cash loans with easy procedures. The reciprocity that users must provide is by paying the loan in installments every month. In this case, Shopee will pay interest to borrowers and if there is a delay in repaying the loan, the borrower will be subject to a fine. The research carried out was field research, this research was descriptive in nature, data analysis in this research used a qualitative descriptive approach. The approach in this research is to obtain and collect data directly or directly from shopee loan users. Shopee's practice of borrowing from the Shopee application is that loans start from 500,000 – 1,500,000.- with monthly installments and with different interest starting from 10%-30% and if you are late in paying you will be subject to a fine of 3% of the nominal amount to be repaid. Review of Islamic Economic Law regarding the practice of borrowing hope from the Shopee application applies qardh conditions, namely the existence of borrowers, lenders, goods/debts, ijab and qabul. But in this practice there is interest which gives rise to usury, so this practice is a practice that is clearly prohibited in Islam. Based on this research, it can be stated that the practice of debts and receivables in the Shopee loan application including usury. The reason is because interest is in addition to the principal of the loaned capital. Keywords: Shopee Borrow, Marketplace, Islamic Economy

The Role of Commercial Bank Loans in Enhancing Financial Performance of Small and Medium-sized Enterprises in Morogoro Municipality, Tanzania

Purpose: This study investigates the role of commercial bank loans on the performance of small and medium-sized enterprises (SMEs) in Morogoro Municipality, Tanzania. It examines how commercial bank loans affect SME financial performance, providing a comprehensive understanding of how financial support from banks influences SME growth and sustainability. Design/Methodology/Approach: The study adopted a quantitative approach. It utilized cross-section data collected from a sample of 92 SMEs in Morogoro municipality. The collected data were descriptively analyzed using SPSS version 25. This analysis aimed to evaluate loan accessibility, determinants of loan choice, and the effect of commercial bank loans on key financial metrics such as profitability, cash flow management, and business growth. Findings: The study reveals that although many SMEs perceive commercial bank loans as easily accessible, others encounter significant barriers, including high interest rates and stringent collateral requirements. The study also reveals that commercial bank loans have mixed effects on SME performance. While they positively impact revenue generation and asset growth, their effects on profitability and business expansion are inconsistent. Originality/Value: This study contributes to the existing literature by providing nuanced insights into the role of commercial bank loans in SME performance within a Tanzanian context. It highlights the challenges SMEs face in accessing financial support and the varying impact of bank loans on their financial outcomes. The study findings are relevant for both policymakers and financial institutions aiming to improve loan accessibility and support SME development. Policy Implications: The study suggests several policy implications, including the need for banks to simplify loan application processes, offer more flexible loan products and address high interest rates that hinder SMEs access to finance. Additionally, it emphasizes the importance of building strong relationships between SMEs and financial institutions and leveraging financial advisory services to enhance loan utilization and overall business performance.

Loan Defaults Prediction Based on Stacked Models Trained by Personalized Features

Training one machine learning model with features that all clients have will result in a waste of features, which is likely to adversely affect the model’s performance. To solve the problem, the study attempts a new method, which it to train an individual stacked model for each loan client based on personalised features. Data used contains information of about fifteen million loan applicants, their default status, and 468 features in all. 41 of the features that can be quantitively analysed are selected according to the feature importance output by a Random Forest model. Default prediction of every client is made by a stacked model trained with all selected features he/she has. The stacked model consists of two layers, in which a Light Gradient-Boosting Machine (LGBM) classifier is the base learner, and a Logistic Regression model is the meta learner. As the defaulters account for only 3.14%, which is significantly unbalanced, Area Under the Curve (AUC) and F1 scores are employed to evaluate the method, instead of accuracy. Test results show that models trained by personalised features perform better than the ones trained by shared features. Additionally, the stacked model outperforms individual Logistic Regression model, but performs nearly the same as individual LGBM Classifier. In detailed, the stacked models trained with personalised features result in AUC=0.772 and F1=0.188. Due to data unbalance, although the method’s F1 score is relatively low, it’s considered to be passable. In the future, stacked models combining different models will be attempted.

Impact of Government Enterprise and Empowerment Programme (GEEP) on poverty status of beneficiaries in Cross River State, Nigeria

In line with the sustainable development goal to end poverty in all forms, the Nigerian Government launched Government Enterprise and Empowerment Programme (GEEP) - as a social investment programme to alleviate poverty by providing credit to petty traders to support entrepreneurship. This study assesses the impact of Government Enterprise and Empowerment Programme (GEEP) on the poverty status of beneficiaries in Cross River State, Nigeria. Specifically, it compared the mean income before, and after accessing the loan, analysed the determinants of poverty status of the respondents in the study area, enumerated the benefits of GEEP to the respondents, and identified the challenges faced by the beneficiaries in accessing the loan. Stratified random sampling technique was used to select 398 respondents who had benefitted from the programme across the eleven Local Government Areas (LGAs) that the programme covered. Data were obtained from primary source with the aid of a well-structured questionnaire, and analyzed using; frequency count, mean, Foster Greer and Thorbecke (FGT) model, paired T- test (Z), and logistic regression analysis. The results showed that, majority of the respondents were male (55.6%) with a mean age of 35 years, and had an average household size of 5 persons. The study also showed that 98.9% of the respondents were educated. The mean monthly income before GEEP support was N 44,535.53, while after GEEP it was N54180.20. Thirty-six percent of the total respondents were poor (36%). The comparative analysis of the mean income before and after accessing the loan showed that, there was a significant difference between their mean incomes at 5% level of significance. Key variables that determined poverty status were; household size and loan amount. The main benefits of GEEP to the respondents were; increase in beneficiaries’ income, and improvement in their standard of living. The major challenges faced by the respondents were; lack of appropriate means to repay loan, stressful loan procedure, and difficulty in transferring money from mobile wallet to bank. The study recommended that the loan application process should be less complex to enable quick loan application. The GEEP programme should be sustained, and allowed to have wider coverage in both rural and urban areas of the country as it has assisted a lot of small business owners’ access to cheap credit. Key words: Impact, GEEP, empowerment programme, government enterprise, poverty status

A Hybrid Extreme Gradient Boosting Model for Credit Risk Modelling in the Presence of Inflation

The recent developments in the credit and banking industry brought by technology has led to increased competition and the rise of risks and challenges. Credit scoring is one of the core items that keeps this industry competitive and profitable. The creation of credit score models to assess the ability of the loan applicant to repay his or her loan remains an active field of research. Practically, the existing models ignore the factor of inflation in determining the credit score of a loan applicant. Inflation affect the performance of the financing institution negatively because it makes some of the borrowers struggle to repay the loan and so leading to some bad debts that might end up being written off. By integrating the inflation factor to the Extreme gradient boosting algorithm led to improved accuracy of the model. In this paper, a new model that uses the inflation rate of a specific region or country in the regularization term of the extreme gradient boosting model has been developed. The evaluation of the model is by comparison with the other common models using ROC, Accuracy, precision and recall. The developed model emerge the second best in terms of performance but better than the standard extreme gradient boosting model.