Search results for: credit cooperatives

Authors: Tina Vuko, Marija Maretić, Marko Čular

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The aim of this study is to analyze the role and effectiveness of internal mechanism (audit committee) of corporate governance on credit institutions performance in Croatia. Based on research objective, sample of 78 credit institutions listed on Zagreb Stock Exchange, from 2007 to 2012, has been collected and efficiency index of audit committee (EIAC) has been created. Based on the sample and created EIAC, conclusions are as follows: audit committees of credit institutions have medium efficiency, based on EIAC measurement; there is a significant difference in audit committee effectiveness, in observed period; there is no positive relationship between audit committee effectiveness and credit institution performance; there is a significant difference between level of audit committee effectiveness and audit firm type. Future research should contain increased number of elements in EIAC creation and increased sample, for all obligators who need to establish audit committee.

Keywords: corporate governance, audit committee, financial institutions, efficiency index of audit committee

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Authors: Fatima Ezzahra Ouboutaib, Abdellatif Aitheda, Soumiya Mekkaoui

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Marketing research is starting to focus on authenticity to position an offer, especially local products. However, its use remains more problematic with the internet for cooperatives. This paper investigates how digitalization impacts the satisfaction of the quest for authenticity. On the theoretical level, it explains authenticity in online and offline contexts in the postmodernism era. Then, an exploratory qualitative study tries to understand the contribution of digitization to the satisfaction of the search for authenticity. Therefore, cooperatives selling terroir products on the Internet are advised to keep also direct contact which tends to show a traditional manner of production to enhance customers’ perception of terroir product authenticity.

Keywords: authenticity, terroir product, online authenticity, postmodernism

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Authors: Essam Al Daoud

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Gradient boosting methods have been proven to be a very important strategy. Many successful machine learning solutions were developed using the XGBoost and its derivatives. The aim of this study is to investigate and compare the efficiency of three gradient methods. Home credit dataset is used in this work which contains 219 features and 356251 records. However, new features are generated and several techniques are used to rank and select the best features. The implementation indicates that the LightGBM is faster and more accurate than CatBoost and XGBoost using variant number of features and records.

Keywords: gradient boosting, XGBoost, LightGBM, CatBoost, home credit

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Authors: O. Badagadze, G. Sirbiladze, I. Khutsishvili

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The work proposes a decision support methodology for the credit risk minimization in selection of investment projects. The methodology provides two stages of projects’ evaluation. Preliminary selection of projects with minor credit risks is made using the Expertons Method. The second stage makes ranking of chosen projects using the Possibilistic Discrimination Analysis Method. The latter is a new modification of a well-known Method of Fuzzy Discrimination Analysis.

Keywords: expert valuations, expertons, investment project risks, positive and negative discriminations, possibility distribution

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Authors: Neha Ahirwar

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In the contemporary digital era, the rise of credit card fraud poses a significant threat to both financial institutions and consumers. As fraudulent activities become more sophisticated, there is an escalating demand for robust and effective fraud detection mechanisms. Advanced machine learning algorithms have become crucial tools in addressing this challenge. This paper conducts a thorough examination of the design and evaluation of a credit card fraud detection system, utilizing four prominent machine learning algorithms: random forest, logistic regression, decision tree, and XGBoost. The surge in digital transactions has opened avenues for fraudsters to exploit vulnerabilities within payment systems. Consequently, there is an urgent need for proactive and adaptable fraud detection systems. This study addresses this imperative by exploring the efficacy of machine learning algorithms in identifying fraudulent credit card transactions. The selection of random forest, logistic regression, decision tree, and XGBoost for scrutiny in this study is based on their documented effectiveness in diverse domains, particularly in credit card fraud detection. These algorithms are renowned for their capability to model intricate patterns and provide accurate predictions. Each algorithm is implemented and evaluated for its performance in a controlled environment, utilizing a diverse dataset comprising both genuine and fraudulent credit card transactions.

Keywords: efficient credit card fraud detection, random forest, logistic regression, XGBoost, decision tree

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Authors: Nasir Mehmood, Muhammad Yar Khan, Anam Javeed

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This study aims to scrutinize the dynamics which tend to impact customer’s purchasing intention of Islamic credit card and nexus of product’s knowledge and religiosity with the attitude of potential Islamic credit card’s customer. The theory of reasoned action strengthened the idea that intentions due to its proven predictive power are most likely to instigate intended consumer behavior. Particularly, the study examines the relationships of perceived financial cost (PFC), subjective norms (SN), and attitude (ATT) with the intention to purchase Islamic credit cards. Using a convenience sampling approach, data have been collected from 450 customers of banks located in Rawalpindi and Islamabad. A five-point Likert scale self-administered questionnaire was used to collect the data. The data were analyzed using the Statistical Package of Social Sciences (SPSS) through the procedures of principal component and multiple regression analysis. The results suggested that customer’s religiosity and product knowledge are strong indicators of attitude towards buying Islamic credit cards. Likewise, subjective norms, attitude, and perceived financial cost have a significant positive impact on customers’ purchase intent of Islamic bank’s credit cards. This study models a useful path for future researchers to further investigate the underlined phenomenon along with a variety of psychodynamic factors which are still in its infancy, at least in the Pakistani banking sector. The study also provides an insight to the practitioners and Islamic bank managers for directing their efforts toward educating customers regarding the use of Islamic credit cards and other financial products.

Keywords: attitude, Islamic credit card, religiosity, subjective norms

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Authors: Niloofar Yousefi, Marie Alaghband, Ivan Garibay

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With the increase of credit card usage, the volume of credit card misuse also has significantly increased, which may cause appreciable financial losses for both credit card holders and financial organizations issuing credit cards. As a result, financial organizations are working hard on developing and deploying credit card fraud detection methods, in order to adapt to ever-evolving, increasingly sophisticated defrauding strategies and identifying illicit transactions as quickly as possible to protect themselves and their customers. Compounding on the complex nature of such adverse strategies, credit card fraudulent activities are rare events compared to the number of legitimate transactions. Hence, the challenge to develop fraud detection that are accurate and efficient is substantially intensified and, as a consequence, credit card fraud detection has lately become a very active area of research. In this work, we provide a survey of current techniques most relevant to the problem of credit card fraud detection. We carry out our survey in two main parts. In the first part, we focus on studies utilizing classical machine learning models, which mostly employ traditional transnational features to make fraud predictions. These models typically rely on some static physical characteristics, such as what the user knows (knowledge-based method), or what he/she has access to (object-based method). In the second part of our survey, we review more advanced techniques of user authentication, which use behavioral biometrics to identify an individual based on his/her unique behavior while he/she is interacting with his/her electronic devices. These approaches rely on how people behave (instead of what they do), which cannot be easily forged. By providing an overview of current approaches and the results reported in the literature, this survey aims to drive the future research agenda for the community in order to develop more accurate, reliable and scalable models of credit card fraud detection.

Keywords: Credit Card Fraud Detection, User Authentication, Behavioral Biometrics, Machine Learning, Literature Survey

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Authors: Hai Chien Pham, Thi Phuong Anh Vo, Chansik Park

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Risks management in construction projects is important to ensure the positive feasibility of the projects in which financial risks are most concerned while construction projects always run on a credit basis. Credit risks, therefore, require unique and technical tools to be well managed. Underwriting technique in credit risks, in its most basic sense, refers to the process of evaluating the risks and the potential exposure of losses. Risks analysis and underwriting are applied as a must in banks and financial institutions who are supporters for constructions projects when required. Recently, construction organizations, especially contractors, have recognized the significant increasing of credit risks which caused negative impacts to project performance and profit of construction firms. Despite the successful application of underwriting in banks and financial institutions for many years, there are few contractors who are applying this technique to analyze and mitigate the credit risks of their potential owners before signing contracts with them for delivering their performed services. Thus, contractors have taken credit risks during project implementation which might be not materialized due to the bankruptcy and/or protracted default made by their owners. With this regard, this study proposes a model using the underwriting technique for contractors to analyze and assess credit risks of their owners before making final decisions for the potential construction contracts. Contractor’s underwriters are able to analyze and evaluate the subjects such as owner, country, sector, payment terms, financial figures and their related concerns of the credit limit requests in details based on reliable information sources, and then input into the proposed model to have the Overall Assessment Score (OAS). The OAS is as a benchmark for the decision makers to grant the proper limits for the project. The proposed underwriting model is validated by 30 subjects in Asia Pacific region within 5 years to achieve their OAS, and then compare output OAS with their own practical performance in order to evaluate the potential of underwriting model for analyzing and assessing credit risks. The results revealed that the underwriting would be a powerful method to assist contractors in making precise decisions. The contribution of this research is to allow the contractors firstly to develop their own credit risk management model for proactively preventing the credit risks of construction projects and continuously improve and enhance the performance of this function during project implementation.

Keywords: underwriting technique, credit risk, risk management, construction project

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Authors: Margarita Gáfaro, Karelys Guzmán, Paola Poveda

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This paper establishes the theoretical aspects that explain the barriers to accessing credit for small agricultural producers in developing countries and identifies successful policy experiences to mitigate them. We will test two hypotheses. The first one is that information asymmetries, high transaction costs and high-risk exposure limit the supply of credit to small agricultural producers in developing countries. The second hypothesis is that low levels of financial education and productivity and high uncertainty about the returns of agricultural activity limit the demand for credit. To test these hypotheses, a review of the theoretical and empirical literature on access to rural credit in developing countries will be carried out. The first part of this review focuses on theoretical models that incorporate information asymmetries in the credit market and analyzes the interaction between these asymmetries and the characteristics of the agricultural sector in developing countries. Some of the characteristics we will focus on are the absence of collateral, the underdevelopment of the judicial systems and insurance markets, and the high dependence on climatic factors of production technologies. The second part of this review focuses on the determinants of credit demand by small agricultural producers, including the profitability of productive projects, security conditions, risk aversion or loss, financial education, and cognitive biases, among others. There are policies that focus on resolving these supply and demand constraints and managing to improve credit access. Therefore, another objective of this paper is to present a review of effective policies that have promoted access to credit for smallholders in the world. For this, information available in policy documents will be collected. This information will be complemented by interviews with officials in charge of the design and execution of these policies in a subset of selected countries. The information collected will be analyzed in light of the conceptual framework proposed in the first two parts of this section. The barriers to access to credit that each policy attempts to resolve and the factors that could explain its effectiveness will be identified.

Keywords: agricultural economics, credit access, smallholder, developing countries

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Authors: Adewale Oladapo, C. A. Afolami

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The study analyzed the effect of micro credit access on poverty reduction among small scale women entrepreneurs in Ondo state, Nigeria. Primary data were collected in a cross-sectional survey of 100 randomly selected woman entrepreneurs. These were drawn in multistage sampling process covering four local government areas (LGAS). Data collected include socio economics characteristics of respondents, access to micro credit, sources of micro credit, and constraints faced by the entrepreneur in sourcing for micro credit. Data were analyzed using descriptive statistics, Foster, Greer and Thorbecke (FGT) index of poverty measure, Gini coefficients and probit regression analysis. The study found that respondents sampled for the survey were within the age range of 31-40 years with mean age 38.6%. Mostly (56.0%) of the respondents were educated to the tune of primary school. Majority (87.0%) of the respondents were married with fairly large household size of (4-5). The poverty index analysis revealed that most (67%) of the sample respondents were poor. The result of the Probit regression analyzed showed that income was a significant variable in micro credit access, while the result of the Gini coefficient revealed a very high income inequality among the respondents. The study concluded that most of the respondents were poor and return on investment (income) was an important variable that increased the chance of respondents in sourcing for micro-credit loan and recommended that income realized by entrepreneur should be properly documented to facilitate loan accessibility.

Keywords: entrepreneurs, income, micro-credit, poverty

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Authors: Titus Wuyah Yunana

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This study analyzed the impact of commercial agricultural credit scheme on income of beneficiary farmers in Kaduna State using the Net farm income and double difference method. A questionnaire was used to source the data from 306 farmers comprising of 153 beneficiaries and 153 non-beneficiaries. The results indicated that the net farm income of the commercial agricultural credit scheme beneficiaries increases from N15,006,352.00 before scheme to N24,862,585.00 after the first and the second phases of the scheme. There was also an increase in the net farm income of the non-beneficiaries from N9, 670,385.40 to N14, 391,469.00 during the scheme. The double difference method analysis indicated a positive mean income difference value between beneficiaries and nonbeneficiaries after the first and the second phases of the scheme. The study recommends expansion in the number of beneficiaries and efficient allocation and utilization of the resources. The government should also introduce more programs that will assist the farmers to increase their productivity, income and the economy as a whole.

Keywords: agriculture, credit scheme, farmers, income, beneficiary

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Authors: Adewale Oladapo, Carolyn A. Afolami

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Agricultural production should be the major financial contributor to the Nigerian economy; however, the petroleum sector had taken the importance attached to this sector. The situation tends to be more worsening unless necessary attention is given to adequate credit supply among food crop farmers. This research analyses the effect of credit use on the technical efficiency of cassava farmers in Ondo State, Nigeria. Primary data were collected from two hundred randomly selected cassava farmers through a multistage sampling procedure in the study area. Data were analysed using descriptive statistics and stochastic frontier analysis (SFA). Findings revealed that 95.0% of the farmers were male while 56.0% had no formal education and were married. The SFA showed that cassava farmer’s efficiency increased with farm size, herbicide and planting material at 5%,10% and 1% respectively but decreased with fertilizer application at 1% level while farmers’ age, education, household size, experience and access to credit increased technical inefficiency at 10%. The study concluded that cassava farmers are technically inefficient in the use of farm resources and recommended that adequate and workable agricultural policy measures that will ensure availability and efficient fertilizer distribution should be put in place to increase efficiency. Furthermore, the government should encourage youth participation in cassava production and ensure improvement in farmer’s access to credit to increase farmer’s technical efficiency.

Keywords: agriculture, access to credit, cassava farmers, technical efficiency

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Authors: Eneken Titov, Ly Hobe

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The collaboration between the universities and companies has been a challenging topic for many years, and although we have many good experiences, those seem to be single examples between one university and company. In Ülemiste Smart City in Estonia, the new initiative was started in 2020 fall, when five Estonian universities cooperated, led by the Ülemiste City developing company Mainor, intending to provide charge-free university courses for the Ülemiste City companies and their employees to encourage university-company wider collaboration. Every Ülemiste City company gets a certain number of free educational credit hours per year to participate in university courses. A functional and simple web platform was developed to mediate university courses for the companies. From January 2021, the education credit platform is open for all Ülemiste City companies and their employees to join, and universities offer more than 9000 hours of courses (appr 150 ECTS). Just two months later, more than 20% of Ülemiste City companies (82 out of 400) have joined the project, and their employees have registered for more than in total 3000 hours courses. The first results already show that the project supports the university marketing and the continuous education mindset in general, whether 1/4 of the courses are paid courses (e.g., when the company is out of free credit).

Keywords: education, educational credit, smart city, university-industry collaboration

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Authors: Aida Alemany, Laura Ballester, Ana González-Urteaga

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From 2007 subprime crisis to the recent Eurozone debt crisis the European banking industry has experienced a terrible financial instability situation with increasing levels of CDS spreads (used as a proxy of credit risk). This paper investigates whether volatility transmission channels in European banking markets have changed after three significant crises’ events during the period January 2006 to March 2013. The global financial crisis is characterized by a unidirectional volatility shocks spillovers effect in credit risk from inside to outside the Eurozone. By contrast, the Eurozone debt crisis is revealed to be local in nature with the euro as the key element suggesting a market fragmentation between distressed peripheral and non-distressed core Eurozone countries, whereas retaining the local currency have acted as a firewall. With these findings we are able to shed light on the impact of the different crises on the European banking credit risk dynamics.

Keywords: CDS spreads, credit risk, volatility spillovers, financial crisis

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Authors: M. Chumburidze, N. Salia, T. Namchevadze

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This work deal with case processing problems in business. The task of strategic credit requirements management of cases portfolio is discussed. The information model of credit requirements in a binary tree diagram is considered. The algorithms to solve issues of prioritizing clusters of cases in business have been investigated. An implementation of priority queues to support case management operations has been presented. The corresponding pseudo codes for the programming application have been constructed. The tools applied in this development are based on binary tree ordering algorithms, optimization theory, and business management methods.

Keywords: credit network, case portfolio, binary tree, priority queue, stack

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Authors: A. Bolaji Adesoye, Alimi Olorunfemi

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This paper examines the impact of small and medium-scale enterprises (SMEs) credit financing and financial market development and their shocks on the output growth of Nigeria. The study estimated a VAR model for Nigeria using 1970-2013 annual data series. Unit root tests and cointegration are carried out. The study also explores IRFs and FEVDs in a system that includes output, commercial bank loan to SMEs, domestic credit to private sector by banks, money supply, lending rate and investment. Findings suggest that shocks in commercial bank credit to SMEs has a major impact on the output changes of Nigeria. Money supply shocks also have a sizeable impact on output growth variations amidst other financial instruments. Lastly, neutrality of investment does not hold in Nigeria as it also has impact on output fluctuations.

Keywords: SMEs financing, financial development, investment, output, Nigeria

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Authors: Noelia Collado-Rodriguez

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The Mortgage Credit Directive 2014/17/UE should have been transposed the 21st of March of 2016. However, in Spain not only we did not meet the deadline, but currently we just have a preliminary draft of the so-called Mortgage Credit Act. Before we analyze the preliminary draft from the standpoint of the responsible lending principle, we should point out that this preliminary draft is not a consumer law statute. Through the text of the preliminary draft we cannot see any reference to the consumer, but we see references to the borrower. Furthermore, and more important, the application of this statute would not be, according to its text, circumscribed to borrowers who address the credit to a personal purpose. Instead, it seems that the preliminary draft aims to be one more of the rules of banking transparency that already exists in the Spanish legislation. In this sense, we can also mention that the sanctions contained in the preliminary draft are referred to these laws of banking ordination and oversight – where the rules of banking transparency belong –. This might be against the spirit of the Mortgage Credit Directive, which allows the extension of its scope to credits aimed to acquire other immovable property beyond the residential one. However, the borrower has to be a consumer accordingly with the Directive. It is quite relevant that the prospective Spanish Mortgage Credit Act might not be a consumer protection statute; specially, from the perspective of the responsible lending principle. The responsible lending principle is a consumer law principle, which is based on the structural weakness of the consumer’s position in the relationship with the creditor. Therefore, it cannot surprise that the Spanish preliminary draft does not state any of the pre contractual conducts that express the responsible lending principle. We are referring to the lender’s duty to provide adequate explanations; the consumer’s suitability test; the lender’s duty to assess consumer’s creditworthiness; the consultation of databases to perform the creditworthiness assessment; and the most important, the lender’s prohibition to grant credit in case of a negative creditworthiness assessment. The preliminary draft just entitles the Economy Ministry to enact provisions related to those topics. Thus, the duties and rules derived from the responsible lending principle included in the EU Directive will not have legal character in Spain, being mere administrative regulations. To conclude, the two main questions that come up after reading the Spanish Mortgage Credit Act preliminary draft are, in the first place, what kind of consequences might arise from the Mortgage Credit Act if finally it is not a consumer law statute. And in the second place, what might be the consequences for the responsible lending principle of being developed by administrative regulations instead of by legislation.

Keywords: consumer credit, consumer protection, creditworthiness assessment, responsible lending

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Authors: Parisa Golbayani, Dan Wang, Ionut¸ Florescu

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In this work we implement machine learning techniques to financial statement reports in order to asses company’s credit rating. Specifically, the work analyzes the performance of four neural network architectures (MLP, CNN, CNN2D, LSTM) in predicting corporate credit rating as issued by Standard and Poor’s. The paper focuses on companies from the energy, financial, and healthcare sectors in the US. The goal of this analysis is to improve application of machine learning algorithms to credit assessment. To accomplish this, the study investigates three questions. First, we investigate if the algorithms perform better when using a selected subset of important features or whether better performance is obtained by allowing the algorithms to select features themselves. Second, we address the temporal aspect inherent in financial data and study whether it is important for the results obtained by a machine learning algorithm. Third, we aim to answer if one of the four particular neural network architectures considered consistently outperforms the others, and if so under which conditions. This work frames the problem as several case studies to answer these questions and analyze the results using ANOVA and multiple comparison testing procedures.

Keywords: convolutional neural network, long short term memory, multilayer perceptron, credit rating

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Authors: Sami Mestiri, Abdeljelil Farhat

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The aim of this current paper is to predict the credit risk of banks in Tunisia, over the period (2000-2005). For this purpose, two methods for the estimation of the logistic regression model with random effects: Penalized Quasi Likelihood (PQL) method and Gibbs Sampler algorithm are applied. By using the information on a sample of 528 Tunisian firms and 26 financial ratios, we show that Bayesian approach improves the quality of model predictions in terms of good classification as well as by the ROC curve result.

Keywords: forecasting, credit risk, Penalized Quasi Likelihood, Gibbs Sampler, logistic regression with random effects, curve ROC

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Authors: Paramita Nur Kurniati, Stanislaus Riyanta

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In the midst of the heightened usage of electronic money (e-money), Indonesian government expenditure is yet governed through cash-basis transactions. This conventional system brings about a number of potential risks and obstacles to operational conduct, including state financial liquidity issue. Consequently, Ministry of Finance is currently establishing the cashless payment methods for State Budget (APBN). Included in those advance methods is credit card facility as a government expenditure payment scheme. This policy is one of the innovations within the public sector learned from other countries’ best practices. Moreover, this particular method is already prominent within the private-sector realm. Qualitative descriptive analysis technique is implemented to evaluate the contemporary innovation of using government credit card in the path towards cashless society. This approach is expected to generate several benefits for the government, particularly in minimizing corruption within the state financial management. Effective coordination among policy makers and policy implementers is essential for the success of this policy’s exercise, without neglecting prudence and public transparency aspects. Government credit card usage shall be the potent resolution for enhancing the government’s overall public service performance.

Keywords: cashless basis, cashless society, government credit card, public sector innovation

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Authors: Abdus-Samiᶜi Imam Arikewuyo

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Islam enjoins the spirit of cooperation among fellow mankind. This is particularly entrenched in the brotherhood phenomenon advocated by Islam. With cooperation, a group of people with diverse qualities and opportunities can facilitate a breakthrough in what is seemingly difficult, if not impossible. This understanding underscores the initiative of establishing multi-purpose cooperative societies for thrifts and savings among bonafide members, especially in developing nations. The spirit and objectives of the Multi-Purpose Cooperatives Societies gave birth to the founding of several of these organizations as a plausible economic outfit in Nigeria to provide succor to the socio-economic predicaments of members. Pertinently, many Islamic cooperative societies sprang up, carving a niche for themselves as Shariᶜah-based economic outfits to accommodate the yearnings of the Muslim populace. KWASU Al-Halal Cooperative Society, Malete, Nigeria, is one such organization that is not only Shariᶜah inclined but also institutional-based. This paper, therefore, aims to examine the operations and activities of this society with a view to determining its strength as a Shariᶜah-based economic outfit for the survival of its clients in a competitive multi-religious atmosphere. The study is both historical and descriptive; thus, it employed interview, observation, focus group discussion and hermeneutical methods as pertinent research tools. The research findings indicated that adopting the Shariᶜah prescriptions, KWASU Al-Halal Cooperative Society, as a representation of the Islamic cooperatives society, fulfilled the essence and goal of its establishment, serving as an outlet for meeting the socio-economic demands of its members regardless of religious inclinations. The paper recommended that stronger efforts should be made on proper record keeping, the conscientiousness of beneficiaries on loan refunds or payments on purchases and education of the loanees and guarantors.

Keywords: efficacy, Islamic cooperatives society, Kwasu al-halal cooperative society, shariᶜah

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Authors: R. H. Fattepur, Sameer R. Fattepur, D. K. Sreekantha

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Dairy Farming is one of the key industries in India. India is the leading producer and also the consumer of milk, milk-based products in the world. In this paper, we have attempted to the replace the human expert system and to develop an artificial expert system prototype to increase the speed and accuracy of decision making dairy farming credit risk evaluation. Fuzzy logic is used for dealing with uncertainty, vague and acquired knowledge, fuzzy rule base method is used for representing this knowledge for building an effective expert system.

Keywords: expert system, fuzzy logic, knowledge base, dairy farming, credit risk

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Authors: Hyunchul Ahn, William X. S. Wong

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Corporate credit rating prediction is one of the most important topics, which has been studied by researchers in the last decade. Over the last decade, researchers are pushing the limit to enhance the exactness of the corporate credit rating prediction model by applying several data-driven tools including statistical and artificial intelligence methods. Among them, multiclass support vector machine (MSVM) has been widely applied due to its good predictability. However, heuristics, for example, parameters of a kernel function, appropriate feature and instance subset, has become the main reason for the critics on MSVM, as they have dictate the MSVM architectural variables. This study presents a hybrid MSVM model that is intended to optimize all the parameter such as feature selection, instance selection, and kernel parameter. Our model adopts genetic algorithm (GA) to simultaneously optimize multiple heterogeneous design factors of MSVM.

Keywords: corporate credit rating prediction, Feature selection, genetic algorithms, instance selection, multiclass support vector machines

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Authors: Anjali Chouksey, Riya Nimje, Jahanvi Saraf

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Online transactions have increased dramatically in this new ‘social-distancing’ era. With online transactions, Fraud in online payments has also increased significantly. Frauds are a significant problem in various industries like insurance companies, baking, etc. These frauds include leaking sensitive information related to the credit card, which can be easily misused. Due to the government also pushing online transactions, E-commerce is on a boom. But due to increasing frauds in online payments, these E-commerce industries are suffering a great loss of trust from their customers. These companies are finding credit card fraud to be a big problem. People have started using online payment options and thus are becoming easy targets of credit card fraud. In this research paper, we will be discussing machine learning algorithms. We have used a decision tree, XGBOOST, k-nearest neighbour, logistic-regression, random forest, and SVM on a dataset in which there are transactions done online mode using credit cards. We will test all these algorithms for detecting fraud cases using the confusion matrix, F1 score, and calculating the accuracy score for each model to identify which algorithm can be used in detecting frauds.

Keywords: machine learning, fraud detection, artificial intelligence, decision tree, k nearest neighbour, random forest, XGBOOST, logistic regression, support vector machine

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Authors: Fuchun Li, Hong Xiao

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We propose a new early warning model for predicting financial stress events for a given future time. In this model, we examine whether credit conditions play an important role as a nonlinear propagator of shocks when predicting the likelihood of occurrence of financial stress events for a given future time. This propagation takes the form of a threshold regression in which a regime change occurs if credit conditions cross a critical threshold. Given the new early warning model for financial stress events, we evaluate the performance of this model and currently available alternatives, such as the model from signal extraction approach, and linear regression model. In-sample forecasting results indicate that the three types of models are useful tools for predicting financial stress events while none of them outperforms others across all criteria considered. The out-of-sample forecasting results suggest that the credit-regime switching model performs better than the two others across all criteria and all forecasting horizons considered.

Keywords: cut-off probability, early warning model, financial crisis, financial stress, regime-switching model, forecasting horizons

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Authors: Liyu Adhi Kasari Sulung

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Poverty has been a major problem in Indonesia. Islamic micro finance (IMF) named Baitul Maal Wat Tamwil (Bmt) plays a prominent role to eradicate this. Indonesia as the biggest muslim country has many successful applied products such as worldwide adopt group-based lending approach, flexible financing for farmers, and gold pawning. The Problems related to these models are operation risk management and internal control system (ICS). A proper ICS will help an organization in preventing the occurrence of bad financing through detecting error and irregularities in its operation. This study aims to seek a proper risk management scheme of credit system in Bmt and internal control system’s rank for every stage. Risk management variables are obtained at the first In-Depth Interview (IDI) and Focus Group Discussion (FGD) with Shariah supervisory boards, boards of directors, and operational managers. Survey was conducted covering nationwide data; West Java, South Sulawesi, and West Nusa Tenggara. Moreover, Content analysis is employed to build the relationship among these variables. Research Findings shows that risk management Characteristics in Indonesia involves ex ante, credit process, and ex post strategies to deal with risk in credit system. Ex-ante control consists of Shariah compliance, survey, group leader reference, and islamic forming orientation. Then, credit process involves saving, collateral, joint liability, loan repayment, and credit installment controlling. Finally, ex-post control includes shariah evaluation, credit evaluation, grace period and low installment provisions. In addition, internal control order sort three stages by its priority; Credit process as first rank, then ex-post control as second, and ex ante control as the last rank.

Keywords: internal control system, islamic micro finance, poverty, risk management

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Authors: Marija Vicic

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The purpose of commercial credit regulation in an unified way under Law on Foreign Exchange Operations in Republic of Serbia allows an easier state monitoring of credit operations performed by non-professionals on foreign exchange market. By broadly defining the term “commercial credits“, the state (i.e. National Bank of Serbia) is given the authority to monitor the performance of all obligations under commercial contracts in which the obligations are not performed simultaneously. In the first part of the paper, the author analyses the economic gist of commercial credits with the purpose of giving an insight into their special treatment. The author examines the term „commercial credits“ given in Law on foreign exchange operations and the difference between financial credits and irregular commercial credits (exports and imports of goods and services deemed to be commercial credits) is particularly highlighted. In the second part, the author emphasizes the specifics of commercial credit contracts, especially the effects of special requests for the parties to these contracts to notify National Bank of Serbia and specific regulations regarding maturity of obligations under these commercial credits and the assignment and compensation of the said contracts.

Keywords: commercial credit, foreign exchange operations, commercial transactions, deferred payment, advance payment, (non) resident

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Authors: Tan Swee Liang

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This paper studies the two-way relationship between fintech credit and banking efficiency using the Generalized panel Method of Moment (GMM) estimation in structural equation modeling (SEM). Banking system efficiency, defined as its ability to produce the existing level of outputs with minimal inputs, is measured using input-oriented data envelopment analysis (DEA), where the whole banking system of an economy is treated as a single DMU. Banks are considered an intermediary between depositors and borrowers, utilizing inputs (deposits and overhead costs) to provide outputs (increase credits to the private sector and its earnings). Analysis of the interrelationship between fintech credit and bank efficiency is conducted to determine the impact in different country groupings (ASEAN, Asia and OECD), in particular the banking system response to fintech credit platforms. Our preliminary results show that banks do respond to the greater pressure caused by fintech platforms to enhance their efficiency, but differently across the different groups. The author’s earlier research on ASEAN-5 high bank overhead costs (as a share of total assets) as the determinant of economic growth suggests that expenses may not have been channeled efficiently to income-generating activities. One practical implication of the findings is that policymakers should enable alternative financing, such as fintech credit, as a warning or encouragement for banks to improve their efficiency.

Keywords: fintech lending, banking efficiency, data envelopment analysis, structural equation modeling

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Authors: Chow Fah Yee, Eu Chye Tan

Abstract:

The main purpose of developing a modern and sophisticated financial system is to mobilize and allocate the country’s resources for productive uses and in the process contribute to economic growth. Financial liberalization introduced in Malaysia in 1978 was said to be a step towards this goal. According to Mc-Kinnon and Shaw, the deregulation of a country’s financial system will create a more efficient and competitive market driven financial sector; with savings being channelled to the most productive users. This paper aims to assess whether financial liberalization resulted in bank credit being allocated to the more productive users, for the case of Malaysia by: firstly, using Chi-square test to if there exists a relationship between financial liberalization and bank lending in Malaysia. Secondly, to analyze on a comparative basis, the share of loans secured by 9 major economic sectors, using data on bank loans from 1975 to 2003. Lastly, present value analysis and rank correlation was used to determine if the recipients of bigger loans are the more efficient users. Chi-square test confirmed the generally observed trend of an increase in bank credit with the adoption of financial liberalization. While the comparative analysis of loans showed that the bulk of credit were allocated to service sectors, consumer loans and property related sectors, at the expense of industry. Results for rank correlation analysis showed that there is no relationship between the more productive users and amount of loans obtained. This implies that the recipients (sectors) that received more loans were not the more efficient sectors.

Keywords: allocation of resources, bank credit, financial liberalization, economics

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Authors: Minjuan Sun

Abstract:

Ever since the late 1990s, China has experienced explosive growth in consumer lending, especially in short-term consumer loans, among which, the growth rate of non-bank lending has surpassed bank lending due to the development in financial technology. On the other hand, China does not have a universal credit scoring and registration system that can guide lenders during the processes of credit evaluation and risk control, for example, an individual’s bank credit records are not available for online lenders to see and vice versa. Given this context, the purpose of this paper is three-fold. First, we explore if and how alternative digital footprint data can be utilized to assess borrower’s creditworthiness. Then, we perform a comparative analysis of machine learning methods for the canonical problem of credit default prediction. Finally, we analyze, from an institutional point of view, the necessity of establishing a viable and nationally universal credit registration and scoring system utilizing online digital footprints, so that more people in China can have better access to the consumption loan market. Two different types of digital footprint data are utilized to match with bank’s loan default records. Each separately captures distinct dimensions of a person’s characteristics, such as his shopping patterns and certain aspects of his personality or inferred demographics revealed by social media features like profile image and nickname. We find both datasets can generate either acceptable or excellent prediction results, and different types of data tend to complement each other to get better performances. Typically, the traditional types of data banks normally use like income, occupation, and credit history, update over longer cycles, hence they can’t reflect more immediate changes, like the financial status changes caused by the business crisis; whereas digital footprints can update daily, weekly, or monthly, thus capable of providing a more comprehensive profile of the borrower’s credit capabilities and risks. From the empirical and quantitative examination, we believe digital footprints can become an alternative information source for creditworthiness assessment, because of their near-universal data coverage, and because they can by and large resolve the "thin-file" issue, due to the fact that digital footprints come in much larger volume and higher frequency.

Keywords: credit score, digital footprint, Fintech, machine learning

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