Personal Loans Research Articles

Personal Loan Default Prediction Analysis based on TabNet and Logistic Regression

Abstract. With the continuous development of the global economy, the demand for loans from individuals and enterprises is growing. However, loan defaults have gradually become a major challenge facing the financial industry. Loan defaults not only directly affect the profitability of financial institutions, but may also trigger systemic risks and pose a potential threat to the entire economic system. Therefore, improving the accuracy of loan default prediction is crucial for financial institutions to effectively manage credit risks. This study built a system for personal loan default prediction by combining TabNet with the Logistic regression model. Through feature engineering, this study extracts potential credit risk features by utilizing datasets from personal and Internet loans. The accuracy of default prediction is improved by combining TabNet's feature learning capabilities with Logistic Regression's interpretability. An AUC value of 0.89 was achieved by the integrated model, which achieved a notable performance. The results indicate that a system that relies on machine learning to predict defaults can significantly enhance the quality of credit approval decisions and lower the likelihood of bad debts for financial institutions. Future studies could aim to optimize the feature selection process, experiment with more advanced machine learning algorithms, or apply the model to diverse loan datasets, thereby enhancing both its generalization and accuracy. In conclusion, this study offers a novel approach to loan default prediction, demonstrating significant practical value and providing substantial support for the risk management and decision-making processes of financial institutions.

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.

Assessing Financial Literacy of Female Entrepreneurs in Informal Economy: Empirical Evidence from Micro Business in Malaysia

Financial management in business ensures that the goals of an organization are achieved. It has become a priority for every business to have good financial resources to ensure sustainability. However, many entrepreneurs are found to need help managing their finances well and are used to supporting a lavish lifestyle. As an outcome, they had to take a personal loan from a financial institution, and this personal loan is a big commitment because they must pay it back with a high loan rate. This situation puts an additional burden on entrepreneurs, which eventually becomes the cause of business failure. Employing a mixed method, this study's objectives are to evaluate the financial literacy of female entrepreneurs of micro-businesses in Malaysia and to determine the causes of financial illiteracy. This study involved 100 female entrepreneurs in the survey study and 20 female entrepreneurs of micro business in the interview study. The survey data were examined using descriptive and inferential statistics, and interview data were analyzed using thematic analysis. The survey findings have revealed that most female entrepreneurs have a low level of financial literacy, and financial literacy significantly influences business success. Interview data revealed that most informants still need to adopt the systematic accounting system, are unable to organize financial and personal accounts, do not have proper financial savings, and cannot calculate product costing and selling price. Therefore, this study will help female entrepreneurs manage their future financial practices.

Collateral damage. Personal lenders and the creation of national mortgage markets in North America, 1890s-1960s

Between the 1880s and 1950s, national mortgage markets were created in the United States and Canada. The formative role of institutions and federal governments are well understood. Personal (individual or ‘private’) lenders have been omitted from the narrative although, in the late 1940s, they still provided a quarter of residential mortgages in the United States and two fifths in Canada. Rarely targeted, they were collateral damage, especially, of state initiatives. The distinctive nature of mortgages, secured on real estate, made the creation of national markets difficult. The character of personal loans – local, non-standard, non-amortized, and often informal -- made their assimilation into any supra-local market especially challenging. Their significance declined in waves: the 1900s, 1920s, and then, under government influence, after 1945. They persist in the reduced gaps left by institutions. We should pay them more attention.

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.

Living Free from Personal Debt: A Possibility or A Mere Wish?

The steady growth in personal debt over the years in the United States is beginning to raise concerns because people are slowly drowning in debt, and it is taking a huge toll on their everyday lives, their relationships, and their mental health. People are increasingly getting stressed, anxious, and depressed because the debts are overwhelming, and the payments are becoming difficult because of growing inflation and high interest rates. 8 out of every 10 adult Americans are reportedly drowning in debt, and it cuts across young adults to older adults who are retiring with debts. Debt is generally categorized as good debt and bad debt – good debt (student loans, business loans, and mortgages) is owed on things that will build wealth in the future, while bad debt (auto loans, credit cards, personal loans, payday loans) hardly improves one’s financial stability. This paper examines how possible it is to become debt-free and establishes that it is a journey that requires some level of discipline and positivity and will take a while to accomplish for anyone who is determined to get out of debt.

HOW EQUATED MONTHLY INSTALMENT (EMI) SCHEMES AND BUY NOW, PAY LATER (BNPL) SERVICES FUEL IMPULSIVE BUYING: A CLOSER LOOK AT CONSUMER BEHAVIOUR

An Equated Monthly Instalment (EMI) is a fixed payment amount made by a borrower to a lender at a specified date each calendar month. The EMI consists of both the principal amount and the interest on the loan, which is spread over a predetermined period. EMIs are commonly used in home loans, personal loans, and auto loans, where the borrower repays the loan in regular instalments over time. Credit card companies typically offer this facility, but nowadays, it is also available with debit cards. Many banks are now providing a pre-approved credit limit for their debit card holders as well. BNPL stands for "Buy Now, Pay Later". It is a payment option that allows consumers to purchase goods and services and pay for them later, typically with no interest or fees. BNPL schemes offer customers the convenience of paying for purchases over time, without needing a credit card or traditional loan. This payment option has become increasingly popular among consumers, particularly among younger generations, to manage their finances and make larger purchases more affordable. However, it also has potential drawbacks, such as higher spending and risks of missed payments, that should be considered before utilizing this payment option. The objective of this study is to examine the factors that motivate consumers to choose EMI schemes and BNPL services as payment options, and to evaluate their perceived advantages and the risks associated with them. The study primarily relied on primary data collected from 100 samples using convenient sampling, along with secondary sources such as published research reports on BNPL.

Personal Loans (PINPRI) in the Review of Positive Law and Islamic Law

This research aims to answer a problem regarding the practice of pinpri loans that have not been registered with the Financial Services Authority and how Islamic law reviews the reasons people make pinpri loans. The research method used is statute approach analysis and comparison. Based on the results of the study the practice of personal loans (pinpri) is a cash loan that has not been registered with the Financial Services Authority is the practice of lending through individuals and has not been registered or even licensed by the Financial Services Authority so that it can be said to be illegal and violates Financial Services Authority Regulation Number 77 / POJK.01 / 2016 concerning LPMUBTI, then in the practice of personal loans (pinpri) there are additional funds and have regulations that are not by the operational system listed in the cash loan rules. In terms of Islamic law, the reason why people practice personal loans (pinpri) is because of urgent needs, easy disbursement, and consumptive behavior, while this is contrary to the concept of Islamic law in qardh theory. Because in the practice of personal loans (pinpri) that have not been registered with the Financial Services Authority, several elements are not by Islamic law, namely usury, gharar, dharar, and zhulm so the law is haram and harms and brings mudharat to the community

Enhancing Financial Decision-Making: Predictive Modeling for Personal Loan Eligibility with Gradient Boosting, XGBoost, and AdaBoost

This study aims to improve the prediction of personal loan eligibility through the application of advanced machine learning techniques. Accurate prediction of creditworthiness is crucial for financial institutions to mitigate risks and optimize their lending processes. We evaluated three algorithms: Gradient Boosting, XGBoost, and AdaBoost, using a comprehensive dataset containing demographic and banking information. Among these, XGBoost proved to be the most effective model, achieving an accuracy, precision, recall, and F1 score of 0.95. These results demonstrate XGBoost's superior ability to accurately identify individuals likely to repay loans, making it an invaluable tool for enhancing decision-making in loan approvals. By leveraging XGBoost, banks can reduce the risk of defaults, streamline their operations, and provide better customer service, ultimately leading to more efficient and reliable lending strategies.

Impact of Digital Finance on Credit Structure and Risk-Taking in Commercial Banks: An Empirical Analysis

With the rapid growth of new technologies such as big data, digital technology is increasingly permeating our daily lives. Financial institutions, particularly commercial banks, are also seizing opportunities in the wave of digital financial development to provide better financial services. However, this development in digital finance has affected the credit structure and risk-taking of banks. This study aims to empirically analyze the impact of digital finance development on the credit structure and risk-taking of commercial banks. Additionally, the study seeks to discuss how the combined effects of digital finance and credit structure influence bank risk-taking. Empirical analysis is conducted using financial data from commercial banks over the past decade. The analytical methods include collecting secondary data, statistical data processing, and regression analysis to evaluate the relationships between digital finance development, credit structure, and bank risk-taking. The empirical analysis results show that the development of digital finance has promoted an increase in the scale of bank credit and the ratio of personal loans to credit loans. Integrating digital finance with banks has proven to reduce the risk burden banks bear. Furthermore, as digital finance continues to develop, adjustments in the bank's credit structure also affect the risk burden borne by the banks. Based on these findings, it is recommended that commercial banks continue to adopt digital financial technologies to increase their credit scale and optimize their credit structure. Additionally, banks should consider developing policies that support integrating digital technologies to reduce risk burdens and enhance financial stability. Further research is also suggested to explore the long-term impact of digital finance on the overall financial performance and risk of banks

Optimizing Financial Decision Support Systems with Machine LearningDriven Recommendations

Introduction: The research investigates the utility of cosine similarity as an innovative recommendation system designed to assist individuals in making financial choices tailored to their unique preferences and objectives. It embarks on an extensive analysis of diverse datasets encompassing a wide array of financial products, including investment portfolios, credit card offerings, insurance plans, personal loan options, and car loan packages. Each dataset undergoes meticulous feature extraction and preprocessing to optimize the accuracy of the cosine similarity model. Method: The research then applies cosine similarity to calculate the similarity scores between individual financial products, thereby producing personalized recommendations. These recommendations are predicated on a comprehensive spectrum of input variables. The outcomes of these case studies demonstrate the potency of cosine similarity as a foundation for the development of tailored financial guidance systems. Such recommendations empower individuals to make informed decisions that are intrinsically aligned with their distinctive financial aspirations. Results: Ridge and lasso regression algorithms are deployed to develop predictive models for assessing investment preferences and evaluating potential investment returns. Conclusion: The study highlights the necessity for financial institutions and advisory platforms to invest in data quality and algorithmic sophistication to enhance the efficacy and accuracy of these financial recommendations.

Inequality in high-cost borrowing and unemployment insurance generosity in US states during the COVID-19 pandemic.

US consumers may turn to the private market for credit when income and government benefits fall short. The most vulnerable consumers have access only to the highest-cost loans. Prior research on trade-offs of credit with government welfare support cannot distinguish between distinct forms of unsecured credit due to data limitations. Here we provide insight on credit-welfare state trade-offs vis-à-vis unemployment insurance generosity by leveraging a large sample of credit data that allow us to separate credit cards, personal loans and alternative financial services loans and to analyse heterogeneity in credit use by household income. We find that more generous state unemployment insurance benefits were associated with a lower probability of high-cost credit use during the first seven quarters of the coronavirus disease 2019 (COVID-19) pandemic. This inverse association was concentrated among consumers living in low-income households. Our results support theories that public benefits are inversely associated with the use of costly credit.

<p>Overcoming Banking Limitations: the Use of Alternative Finance</p>

The present article explores the relationship between bank credit constraints and the utilization of alternative financing for a sample of Latin American SMEs. It analyzes whether the inability of businesses to obtain bank credit affects the likelihood of turning to other forms of financing, such as informal loans, personal loans, or trade credit. Logit models are estimated to test whether companies facing bank credit constraints can access alternative forms of financing. The results reveal that the probability of using alternative financing is conditioned by both the type of instrument and the nature of the restriction, whether it originates from the supply or the demand.

Analysing Customer Satisfaction Towards Personal Loans: Evidence from Banking Industry

With an emphasis on banking industry, this study intends to examine customer satisfaction levels of those who have taken out personal loans from banking institutions. Customer satisfaction is a crucial indicator of success and longevity in the banking industry. This study explores the specifics of customer satisfaction among recipients of personal loans; wherein, the primary goal is to use operational research methods to analyse the intricate topography of customer satisfaction in the personal loan sector. The study’s secondary objective is to demonstrate how factor analysis, a well-known dimensionality reduction method, may combine these disparate traits into a condensed set of essential components. The work presents practical insights to guide strategic choices intended to improve customer satisfaction and foster long-term growth in the banking industry, focusing on Banking sector.

Understanding Vulnerability to the Poverty Premium: An Analysis of Factors Influencing Use of High-Cost Credit Among Low-Income Individuals

Introduction & BackgroundAccess to affordable credit is essential for individuals living on low incomes to participate in society fully. However, due to their limited credit history and volatile incomes, many cannot access mainstream sources of credit, such as credit cards and personal loans. As a result, they often must rely on more expensive sources of credit such as payday loans, doorstep loans or rent-to-own loans. This leads to them paying more for credit, also referred to as the poverty premium. Incurring the poverty premium exacerbates the financial challenges faced by already vulnerable individuals and can lead to a cycle of financial distress. Identifying the behaviours and factors that lead to a need for high-cost credit can help in identifying individuals who are most vulnerable to incurring the poverty premium. Objectives & ApproachTo achieve this, we rely on anonymized Open Banking transaction data from 100,000 individuals provided by a UK-based social lender.Given the latent nature of vulnerability, we identify indicators of vulnerability to poverty premium which include frequency of overdraft use, previous debt problems, low financial resilience, and indebtedness. We use a copula-based approach to create an index of vulnerability to poverty premium. This is based on weighting the individual indicators using their Spearman rank correlation coefficient. We use a fixed effects model to identify the factors that contribute to this vulnerability, where the index of vulnerability is the dependent variable. Relevance to Digital FootprintsWe use a rich and granular dataset on individual financial transactions to address a key social issue. The findings from this study can inform policy and industry efforts to promote greater credit affordability for these vulnerable individuals. This is particularly important due to the renewed concerns regarding the increased use of high-cost credit by individuals living on low incomes due to COVID-19 and the increased cost of living. ResultsOur findings show that variables related to the financial profile of an individual are important driving factors of the vulnerability to poverty premium. These include the number of salary sources, frequency of salary receipt, benefit receipt and savings frequency. Other variables related to spending behaviour such as gambling, volatility in fixed expenses and high transaction counts all have positive relationships with this vulnerability. Conclusions & ImplicationsThis study is a first step towards examining the determinants of vulnerability to poverty premium by analyzing an Open Banking transaction data set. The innovative feature of this work is the creation of an index of vulnerability to poverty premiums based on various indicators of financial distress and high-cost credit use. Our findings on the relationships between the individual's financial profile and the vulnerability to poverty premium suggest that policymakers should consider targeting interventions for individuals with specific profiles to alleviate the need for high-cost credit. For example, programs to promote regular savings or financial education for gamblers and people with volatile spending behaviour can help assist these individuals manage their finances better.

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 COMMERCIAL BANKS IN THE ECONOMY OF THE REPUBLIC OF UZBEKISTAN

Commercial banks provide trade finance services such as letters of credit and bank guarantees that facilitate international trade. In addition to financing, commercial banks in Uzbekistan also play an important role in promoting financial inclusion. They offer a range of financial products and services to individuals of all income levels, including savings accounts, debit cards and personal loans. It will help promote financial literacy and inclusion in the country. Commercial banks also make a significant contribution to the growth of the economy by attracting savings of the population and business entities. They pool these funds and allow them to be used for investment in various sectors of the economy. This will help stimulate economic growth and development.

Investigation into Forecasting Personal Loan Defaults and Managing Risk Using Bayesian Game Theory

Investigation into Forecasting Personal Loan Defaults and Managing Risk Using Bayesian Game Theory

Towards a causal link between food insecurity and buy-now-pay-later use by young Australians

Purpose This paper aims to understand if buy-now-pay-later (BNPL) services, a digital type of credit that targets young consumers, acts as a protective or a risk factor for food insecurity among young consumers in Australia. Design/methodology/approach The study uses survey data from a representative sample of young consumers aged 18–24 from all internal states and territories in Australia. Propensity score matching is used to test two hypotheses: BNPL drives young consumers to food insecurity, and food insecurity leads young consumers to use BNPL. Findings There is evidence that BNPL use is driving young Australian consumers to experience food insecurity, but there is no evidence of food insecurity driving the use of BNPL services. Practical implications The evidence of BNPL driving young consumers to experience food insecurity calls for the adoption of practices and stronger regulation to ensure that young users from being overindebted. Originality/value Although the link with more traditional forms of credit (such as personal loans) and consumer wellbeing has been explored more broadly, this project is the first attempt to have causal evidence of the link between BNPL and food insecurity in a high-income country, to the best of the authors’ knowledge. This evidence helps to fill the gap about the protective or risky nature of this type of digital financial product, as experienced by young Australians.

Credit Growth and Risk Management in Indian Banks: Analysis Through Sectoral Deployment

This research paper explores the dynamics of sectoral credit deployment and its impact on risk management within the Indian banking sector over three fiscal years (2021-2023). Utilizing data from the Reserve Bank of India's annual reports, the study delves into the growth patterns of credit allocation to agriculture, industry, services, and personal loans, and assesses corresponding risk metrics such as the Gross Non-Performing Assets (NPA) ratio, Provision Coverage Ratio (PCR), and Capital Adequacy Ratio (CRAR). Descriptive statistics, correlation analysis, and regression modelling were employed to identify relationships and impacts. The findings reveal substantial credit growth in the services and personal loan sectors, accompanied by significant improvements in risk management indicators, including a marked reduction in the Gross NPA ratio and enhanced Provision Coverage Ratios. These trends highlight the sector’s increasing resilience and risk preparedness, reflecting broader shifts towards a consumer-driven financial landscape. The paper discusses the implications of these findings for banking stability and economic policy, emphasizing the need for nuanced regulatory frameworks to manage sector-specific risks. Future research could extend this analysis to predict long-term impacts of credit expansion on financial stability using advanced econometric models.