Credit Worthiness Research Articles

Developing Predictive Models for Financial Stability: Integrating Behavioral Analytics into Credit Risk Management

In this article, its author takes an attempt at investigating the use of behavioral analytics to complement credit risk management in the world of finance. Current mainstream credit risk models very much depend on conventional methods that incorporate historical financial information like credit scores and income while ignoring client behavioral contemporaneous patterns essential for modeling their risk levels. By this, the transactional patterns; payment behaviour and digital activities included besides the traditional financial parameters allows the financial institution to make superior models that gives better results on credit worthiness and risk identification earlier. It is crucial for both better risk assessment and for embracing more inclusion within lending networks and financial creativity. Analyzing the informational component of behavioral data as well as its applicability in credit risk modeling and considering the difficulties of implementing it in practice, the article provides an outline of the approaches to telemetry integration.

Measuring Multi-Dimensional Mobile Behavior Effect on Inclusive Finance: Evidence from China

Credit Invisible is one key area that many countries put much effort to solve in decades. According to the 2020 World Bank statistics, for example, there are over 500 million Chinese and 45 million American, classified as credit invisible who don’t have banking and finance history in bank or credit bureau, making them difficult to borrow money from financial institution. Previous studies adopted different non-financial information to evaluate one’s credit worthiness and status to address this issue. However, they provide little information about how real mobile user interactions can be used to solve this issue in inclusive finance. This paper proposes a novel data generative framework to fusion APP data, call detail record data and SMS data with a total of 4,689 attributes derived from a large-scale mobile dataset. We then construct a unique set of mobile behavior-driven credit risk factors based on statistical diversity, intensity, consistency, and regularity of mobile user behavior characterizing user preferences, attitudes, geolocation, and temporal patterns. Empirical analysis demonstrates that the newly discovered mobile behavior factors are useful as new inputs for credit scoring and proves the factors representing new source of positive and negative credit information. Decision tree analysis and Quantile regression are conducted to validate effect of these factors to credit default. It facilitates credit assessment based on non-financial data for the credit invisible people, which promoting inclusive finance to larger community in society. We also analyze implications of mobile user characterization findings in relation to credit default which helps decision makers to optimize credit policy and product design.

Maturity diversification: The use of foreign and domestic bonds

Raising capital to support the execution of an organization's strategy is one of the most important responsibilities of financial managers. In our analysis we examine the characteristics of issued debt, specifically the maturity and market of issuance. Our empirical analysis finds that firms issue bonds to diversify their bond maturity profile using both foreign and domestic markets. Although firms primarily raise debt using their domestic markets, they use both foreign and domestic markets especially at the short and long maturities. Beyond diversifying their maturity portfolio, borrowing decisions are influenced by factors such as asset maturity, credit worthiness, and transparency.

To Study of Credit Ratings in Commercial Buildings in Maharashtra (A Green Building Approach)

Credit rating plays a pivotal role in the decision-making process of stakeholders in the capitalmarket including regulators, issuers and investors. Therefore, it has been focused by the researchersdoing research in the field of finance domain on this emerging concept. Many studies have beenconducted in the Indian context as well as the global arena on rating methodology, importance ofratings, performance of rating agencies, investors’ awareness, etc. This report puts a light on CreditRatings of Commercial Buildings and Quality Parameters of Credit Rating Agencies and their impact onthe project from customer and investor point of view.Focusing on Quality Ratings as a prime factor ofCredit Ratings of Commercial Buildings. The main objective of my project is to prepare a Quality RatingGuide named Q.R.I.C BOOK for benefitting the credit worthiness of companies and for assuring utmostcustomer satisfaction by guiding them to make better decisions.

Prediction of bank credit worthiness through credit risk analysis: an explainable machine learning study

AbstractThe control of credit risk is an important topic in the development of supply chain finance. Financial service providers should distinguish between low- and high-quality customers to predict credit risk accurately. Proper management of credit risk exposure contributes to the long-term viability and profitability of banks, systemic stability, and efficient capital allocation in the economy. Moreover, it benefits the development of supply chain finance. Supply chain finance offers convenient loan transactions that benefit all participants, including the buyer, supplier, and bank. However, poor credit risk management in supply chain finance may cause losses for finance providers and hamper the development of supply chain finance. Machine learning algorithms have significantly improved the accuracy of credit risk prediction systems in supply chain finance. However, their lack of interpretability or transparency makes decision-makers skeptical. Therefore, this study aims to improve AI transparency by ranking the importance of features influencing the decisions made by the system. This study identifies two effective algorithms, Random Forest and Gradient Boosting models, for credit risk detection. The factors that influenced the decision of the models to make them transparent are explicitly illustrated. This study also contributes to the literature on explainable credit risk detection for supply chain finance and provides practical implications for financial institutions to inform decision making.

The determinants of Turkish CDS volatility: An ARDL approach covering COVID period

The accuracy of CDS premiums has been questioned in many former studies. However, we intend to show that the volatilities of these spreads rather than their basis point levels indicate and signal the status of sovereign risk and credit worthiness as they tend to reveal sudden deteriorations in key sovereign and global economic indicators. In that respect we aim to reveal the determinants of Turkish CDS spread volatility by using an ARDL Bounds Test framework. In line with our expectations exchange rate, stock market indice and oil price volatility have significant positive coefficients in the long run whereas US 10-year bond spreads have short run effects up to three lags. Also, our results show that COVID pandemic has remarkably increased Turkish CDS volatility. Moreover, the unorthodox monetary policies adopted after COVID has also raised CDS volatility with persistently high spread levels where a long-term memory effect was prevalent.

CUSTOMER CREDIT WORTHINESS IN THE DIGITAL AGE: A MANAGEMENT APPROACH TO MACHINE LEARNING APPLICATION IN BANKING

CUSTOMER CREDIT WORTHINESS IN THE DIGITAL AGE: A MANAGEMENT APPROACH TO MACHINE LEARNING APPLICATION IN BANKING

Black-Box Testing and Auditing of Bias in ADM Systems

For years, the number of opaque algorithmic decision-making systems (ADM systems) with a large impact on society has been increasing: e.g., systems that compute decisions about future recidivism of criminals, credit worthiness, or the many small decision computing systems within social networks that create rankings, provide recommendations, or filter content. Concerns that such a system makes biased decisions can be difficult to investigate: be it by people affected, NGOs, stakeholders, governmental testing and auditing authorities, or other external parties. Scientific testing and auditing literature rarely focuses on the specific needs for such investigations and suffers from ambiguous terminologies. With this paper, we aim to support this investigation process by collecting, explaining, and categorizing methods of testing for bias, which are applicable to black-box systems, given that inputs and respective outputs can be observed. For this purpose, we provide a taxonomy that can be used to select suitable test methods adapted to the respective situation. This taxonomy takes multiple aspects into account, for example the effort to implement a given test method, its technical requirement (such as the need of ground truth) and social constraints of the investigation, e.g., the protection of business secrets. Furthermore, we analyze which test method can be used in the context of which black box audit concept. It turns out that various factors, such as the type of black box audit or the lack of an oracle, may limit the selection of applicable tests. With the help of this paper, people or organizations who want to test an ADM system for bias can identify which test methods and auditing concepts are applicable and what implications they entail.

ANALISIS KELAYAKAN KREDIT KOPERASI MITRA TANI MANDIRI DENGAN ALGORITMA NAÏVE BAYES

A cooperative is a financial institution located in each village. Cooperatives can provide solutions to the community when the community's economic needs are decreasing. One of the services provided by cooperatives to the community is credit loans. This research focuses on analyzing the suitability of prospective cooperative member creditors who are eligible for credit applications. This analysis requires attributes or variables that are suitable for use in the credit application process. The attributes used in this research are that prospective creditors must be of sufficient age, disciplined in the payment process according to the monthly due date, have income and have allowances. This research uses data mining techniques using the Naïve Bayes algorithm, and data processing uses the Rapidminer application. The results obtained were based on the results of processing 182 data, namely obtaining an accuracy value of 81.32%, precision of 89.36% and recall of 77.78%. In this case, credit worthiness analysis with four attributes using the Naïve Bayes algorithm is suitable for use with accurate and precise results.

Ethno-Racial and Credit Worthiness Disparities in Access to Mortgage Credit

Abstract The mortgage industry is central to ethno-racial stratification in homeownership access. Ample research demonstrates that unequal treatment of minorities has created major differences in the access and exclusion of low-cost loan products in the housing market. While numerous studies have documented the disadvantages Black and Latino homebuyers face, these studies have been limited in their assessment of credit worthiness in accessing a mortgage. This study draws on the annual dataset from the Home Mortgage Disclosure Act from 2018 and 2019 to assess ethno-racial disparities in mortgage outcomes by debt-to-income (DTI) levels. I demonstrate that DTI levels vary tremendously by ethno-racial groups. In addition, I show that loan rejections and high-cost loan originations are highest among Blacks and Latinos across DTI levels compared to White applicants. Depending on the adverse loan outcome, Asians perform similarly or slightly underperform compared to Whites. These trends are particularly true when examining high-cost loan originations as Blacks and Latinos with excellent credit worthiness perform similarly as Whites and Asians with below-average credit worthiness. Implications for ethno-racial stratification are discussed.

DEPOSIT INSURANCE SYSTEM IN GEORGIA AND WORLD TRENDS

The deposit insurance system is one of the main challenges of Georgian Banking regulation, which serves as a safety pillow of financial stability. Over the past 5 years, Georgia has increased the minimum deposit insurance limit from 5,000 GEL to 15,000 GEL per bank in order to achieve greater stability in the financial market. Georgia has already made a decision to increase the limit to 30,000 GEL from January 1, 2024. We examined the impact of the deposit insurance system on the safety of customers of banking institutions by comparing the volume of accounts/deposits protected by the system with existing practices in neighboring economies. We find that increasing attention to the importance of deposit insurance has led to an increase in the interest of foreign depositors and investors in placing funds in Georgian banks, which contributes to the improvement of international ratings and credit worthiness of Georgian banks. Keywords: Deposit insurance, cost of deposit funding, resolution.

The Impact of Taking into Account Information Asymmetries and the Credit Market Structure in the Assessment of Borrowers' Credit Worthiness: Case of Tunisia

We attempt to assess borrowers’ credit worthiness in three scenarios one heralding prevalence of information asymmetries the second heralding absence of information asymmetries and the third a realistic scenario encompassing both features for the sake of determining which one gives the best outcome in terms of goodness of fit and adequacy of the model specification with the corresponding market structure of the credit market in purview. The added value of this research is that, when we want to estimate any entity, the explanatory variables carrying market imperfections must be manifested and not implicitly invocated because of the endogeneity of market imperfections and the market structure should be specified clearly; otherwise, the estimation is inadequate and the empirical results do not correspond to the theoretical predilections. In other words, the conformity of empirical results with theoretical predilections does not rely on the absence of information asymmetries, whose full recognition does not introduce spuriousness into empirical results. The research is a comparative cross investigation of scenarios highlighting the best fit to borrowers' Creditworthiness assessment and deducing through logical analysis the role payed by information asymmetries and the banking market power in shaping this fit.

Development of new payment services and the role of the fintech sector during the COVID-19 pandemic

Purpose: The purpose of the article is to present both theoretical and practical basis for development of payment services in Poland and in the whole world during the COVID-19 pandemic. Over this time there was a sharp rise in e-commerce along with a fast growth of the number of consumers making online payments. This resulted in changes in adoption of new technologies and payment services. Apart from those which were the consequence of quickening the pace of change in the consumers’ behavior, a crucial role, especially in developing countries, was played by local regulators, who somehow forced financial institutions to create new solutions, which fintech readily made good use of. In numerous countries it fostered radical and groundbreaking quickening of the pace of shifting to electronic payments and creating a new sort of payment services, adjusted to the needs and expectations of younger consumers. Methodology: The paper uses the method of an economic analysis and literature review - mostly electronic sources, descriptive and comparative analyses. Findings: From customers’ perspective, the impact of COVID-19 pandemic on development of new payment services should be acknowledged as positive, or even groundbreaking in some developing countries. Over the years 2020-2021 the number of customers making on-line payments, especially mobile payments, grew rapidly. This was the result of a fast, forced by pandemic, growth in sales in e-commerce. Consequently, it enabled to introduce and widely promote new financial services - based on open banking, and promoting solutions targeted at young consumers, e.g. deferred payments. Even though the pandemic is over, the interest rate rise and the consequences of the war in Ukraine are bound to bring further, dynamic development of such payment services, as well as their popularization on more and more new markets all over the world. Practical implications: The analysis of relations between theoretical and practical bases of widespread adoption of new solutions concerning on-line payments in the world during the COVID-19 pandemic is an important factor in the development of e-commerce market and financial services. The fact that digital forms of payments have become very common brought about the network effect, a situation when the value for the customer depends on the number of users of a particular solution. Due to that, poorer, developing countries could, making use of the solutions based on open banking and the technology provided by fintech, quickly popularize cashless, and electronic payments. Only a few years ago it was estimated that in the countries lacking the standard payment infrastructure it would take decades to reach this level. Also, in developed countries the pandemic contributed to popularization among young consumers of solutions adapted to their preferences, being an alternative to traditional credit cards. Deferred payments are a solution which is commonly used in e-commerce, and their main merits include increasing both conversion rates and average order value. However, it needs to be emphasized that this goes with a less flexible business model of the companies providing such services in high interest rate environment, as well as adverse consequences of broad access to such services. Many countries have observed a growth of customer debt - which effects both credit worthiness and problems to pay off rising debts. This results in imposing formal regulations on deferred payments markets more and more frequently.

Penerapan Fitur Seleksi dan Particle Swarm Optimization pada Algoritma Support Vector Machine untuk Analisis Credit Scoring

After the Covid-19 pandemic, the banking sector faced significant challenges in contributing to achieving national goals in terms of increasing living standards and equalizing the regional economy. Hundreds of millions of low-income people have no credit or bank accounts because they do not have sufficient credit history to warrant the credit scores assigned to them. An estimated 1.7 billion people (31% of the adult population) do not have an account with a financial institution. People today are usually concentrated in developing countries, especially in China 204 million, India 357 million and Indonesia 102 million people. Because it is very difficult to make accurate predictions in determining credit worthiness for low-income people. Cooperatives are financial institutions that have a crucial role in channeling financing to members and the community to develop their businesses. An inappropriate credit distribution process can have a negative effect on KSP, resulting in frequent losses. This risk is known as problem loans, the cause is the KSP's failure to analyze the credit of prospective debtors. Therefore, calculations are needed to detect opportunities for credit risk default by prospective debtors objectively and precisely so that loan problems do not occur. Credit scoring is a method used to evaluate credit risk in terms of loan applications from consumers [4]. In this research we will provide a solution using classification techniques with feature selection methods in the Particle Swarm Optimization (PSO) Algorithm and Support Vector Machine (SVM) to predict the credit risk of prospective debtors failing to make loan payments. The application of the SVM algorithm in credit scoring research is because SVM is good at data classification. However, the standard SVM model still does not produce optimal results due to the difficulty of determining the best parameters, therefore researchers will optimize it with the Feature Selection and PSO algorithms to determine the best parameters. The results from the combination of several parameters using PSO-SVM obtained an accuracy of 87.23%, therefore the application of this method was proven to improve the performance of the SVM algorithm to increase its accuracy results in predicting the feasibility of granting credit.

PREDICTING CREDIT WORTHINESS IN BANK LOAN APPROVAL PROCESS

Loans make up significant part in bank profits. In a bank, amongst large number of loan applications, it can be lengthy and challenging to choose genuine and eligible applications to approve the loan, if the process is done manually. The system was developed using Python 3.7 & its libraries and Jupyter Notebook cross-platform Integrated Development Environment (IDE). The developed system uses the machine learning algorithms - Naïve Bayes, Support Vector Machine (SVM) and XGBoost (Extreme Gradient Boosting) individually to classify the loan applications to automatically choose genuine and eligible applications. Based on average bank balance of the period and financial background, the system approves the loan amount to the predicted applicants with good credit history. The system’s performance in predicting the credit worthiness of applicants was evaluated. Naive Bayes algorithm predicted the credit worthiness with 79% accuracy, SVM algorithm with 81% and XGBoost algorithm with 84%.

Uncertainty, credit and investment: Evidence from firm-bank matched data

This paper studies how high uncertainty affects corporate bank loans, addressing the important issue of identification. In times of high uncertainty, firms reduce their credit demand due to delayed investments or a deterioration in credit worthiness. Simultaneously, banks are more exposed to negative shocks to their balance sheet, reducing credit supply. To isolate the uncertainty effect from the credit supply effect, we employ matched bank-firm loan data covering all loans extended by all financial intermediaries to listed firms in Korea, a bank-centered economy. Our empirical results reveal that a failure to control for credit supply leads to overestimating the effect of uncertainty on bank loans. In terms of the transmission channel of uncertainty, we find the evidence of both the real option channel and the financial channel: the negative effect is stronger for firms with a higher degree of investment irreversibility and financially constrained firms. In addition, our findings suggest that larger firms may be predominantly affected by uncertainty shocks through the real option channel rather than the financial channel. In addition, our empirical findings on firm investments align with those on bank loans.

KOMPARASI METODE SAW, TOPSIS DAN WEIGHTED PRODUCT DALAM PENENTUAN PEMBERIAN KREDIT ANGGOTA KOPERASI KARYAWAN

Loans to employees can be assessed as a form of support to foster loyalty and work enthusiasm, but on the other hand employee cooperatives entrusted with managing mandatory savings funds must be able to take into account the credit worthiness value without being subjective in order to minimize the possibility of bad credit risk so that the goal long-term member welfare can be achieved and business can run well. Comparisons using the SAW, Topsis and Weighted Product methods in determining the granting of credit to members of cooperatives employees play an important role in providing fast, precise and accurate information to determine credit granting decisions.

Information acquisition and rating agencies

For decades credit rating agencies were viewed as trusted arbiters of creditworthiness and their ratings as important tools for managing risk. The common narrative is that the value of ratings was compromised by the evolution of the industry to a form where issuers pay for ratings. In this paper we consider both an investor-pays and an issuer-pays set-ups and show that if the investor-pays version can overcome the free-rider problem it is efficient, and otherwise leads to under-provision of information; while if the issuer-pays can force disclosure, it is efficient, but otherwise it leads to less revealing information because of the systematic distortion in revealed information along with over-investment in information. We show that in both these arrangements credit ratings have value in equilibrium and how reputation insures that, in equilibrium, ratings will reflect sound assessments of credit worthiness. We argue that regulatory reliance on ratings and the increasing importance of risk-weighted capital in prudential regulation have more likely contributed to distorted ratings than the matter of who pays for them.

Need for Alternatives to Traditional Credit Scores

As of 2021, more than half of the adult Indian population remained unserved. Only 21.99% of the adult population could be counted as credit served. When we look at the top five Indian banks by total assets, we see that only one bank was utilising more than 60% of its total assets for loans and advances. If banks were able to expand their operations within the unserved population, they could operate at higher leverages. This would mean that the banks would have the ability to operate at lower net interest margins in times of credit crunch and reap higher profits during periods of normalcy. To reach the yet credit unserved, it is essential to gauge their credit worthiness in a manner that does not require credit histories. Hence, in this paper, we will look at some alternatives to traditional credit scoring methods.

LOAN REPAYMENT AND CREDIT WORTHINESS OF FARMERS UNDER BANK OF AGRICULTURE IN ENUGU STATE OF NIGERIA

The loan repayment and credit worthiness of farmers under Bank of Agriculture (BOA) in Enugu State were studied. One hundred and twenty respondents were randomly selected for the study using multi stage random sampling technique. The data for the study were collected using a well-structured questionnaire and interview schedule. Multiple regression analysis and logistic analysis were used to address the objectives of the study. The determinant factors to loan repayment in the study area were off -farm income, educational level and membership of organization. In addition, the factors affecting the farmers’ credit worthiness were distance from the bank, all-weather road, educational level and off-farm income. The need to ensure farmers’ access to educational programme, off- farm income employment and to form or join cooperative society were proffered.