Search results for: home credit

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: Yuan-lin Liu, Ye Li, Tian Xia

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There are some shortcomings in Chinese existing taxi management modes. This paper suggests to establish the third-party comprehensive information management platform and put forward an evaluation model based on credit. Four indicators are used to evaluate the drivers’ credit, they are passengers’ evaluation score, driving behavior evaluation, drivers’ average bad record number, and personal credit score. A weighted clustering method is used to achieve credit level evaluation for taxi drivers. The management of taxi industry is based on the credit level, while the grade of the drivers is accorded to their credit rating. Credit rating determines the cost, income levels, the market access, useful period of license and the level of wage and bonus, as well as violation fine. These methods can make the credit evaluation effective. In conclusion, more credit data will help to set up a more accurate and detailed classification standard library.

Keywords: credit, mobile internet, e-hailing taxi, management mode, weighted cluster

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Authors: Yuri Katz, Kun Liu, Arunram Atmacharan

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Due to illiquidity, mispricing on Credit Markets is inevitable. This creates huge challenges to banks and investors as they seek to find new ways of risk valuation and portfolio management in a post-credit crisis world. Here, we analyze the difference in behavior of the spread-to-maturity in investment and high-yield categories of US corporate bonds between 2014 and 2023. Deviation from the theoretical dependency of this measure in the universe under study allows to identify multiple cases of mispriced credit risk. Remarkably, we observe mispriced bonds in both categories of credit ratings. This identification is supported by the application of the state-of-the-art machine learning model in more than 90% of cases. Noticeably, the ML-driven model-based forecasting of a category of bond’s credit ratings demonstrate an excellent out-of-sample accuracy (AUC = 98%). We believe that these results can augment conventional valuations of credit portfolios.

Keywords: credit risk, credit ratings, bond pricing, spread-to-maturity, machine learning

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Authors: Swathi Sampath, V. Kalaichelvi

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The assessment of the risk posed by a borrower to a lender is one of the common problems that financial institutions have to deal with. Consumers vying for a mortgage are generally compared to each other by the use of a number called the Credit Score, which is generated by applying a mathematical algorithm to information in the applicant’s credit report. The higher the credit score, the lower the risk posed by the candidate, and the better he is to be taken on by the lender. The objective of the present work is to use fuzzy logic and linguistic rules to create a model that generates Credit Scores.

Keywords: credit scoring, fuzzy logic, mortgage, risk assessment

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Authors: Abeer Karaali

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This paper will seek to clarify important key terms such as home schooling and home education as well as the legalities attached to such terms. It will reflect on the recent proposed changes to terminology in NSW, Australia. The various pedagogical approaches to home education will be explored including their prominence in the Australian context. There is a strong focus on literature from Australia. The historical background of home education in Australia will be explained as well as the difference between distance education and home education. The statistics related to home education in Australia will be explored in the scope and compared to the US. The future of home education in Australia will be discussed.

Keywords: alternative education, e-learning, home education, home schooling, online resources, technology

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Authors: Luisa Unda, Kamran Ahmed, Paul Mather

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We examine the effect of board characteristics on the performance and asset quality of credit unions in Australia, using a large sample covering the period 2004-2012. Credit unions are unique in that they are customer-owned financial institutions and directors are democratically elected by members, which is distinctly different from other financial institutions, such as commercial banks. We find that board remuneration, board expertise, and attendance at board meetings have significantly positive impacts on credit union performance and asset quality, while board members who hold multiple directorships (busy directors), have a significant negative impact on credit union performance. Financial performance also improves with larger boards and long-tenured directors in credit unions. All of these relations hold after we control for alternative measures of performance, credit union characteristics and endogeneity problem.

Keywords: credit unions, corporate governance, board of directors, financial performance, Australia, asset quality

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Authors: Ines Gasmi, Salima Smiti, Makram Soui, Khaled Ghedira

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Credit risk is considered as one of the important issues for financial institutions. It provokes great losses for banks. To this objective, numerous methods for credit risk evaluation have been proposed. Many evaluation methods are black box models that cannot adequately reveal information hidden in the data. However, several works have focused on building transparent rules-based models. For credit risk assessment, generated rules must be not only highly accurate, but also highly interpretable. In this paper, we aim to build both, an accurate and transparent credit risk evaluation model which proposes a set of classification rules. In fact, we consider the credit risk evaluation as an optimization problem which uses a genetic programming (GP) algorithm, where the goal is to maximize the accuracy of generated rules. We evaluate our proposed approach on the base of German and Australian credit datasets. We compared our finding with some existing works; the result shows that the proposed GP outperforms the other models.

Keywords: credit risk assessment, rule generation, genetic programming, feature selection

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Authors: Manpreet Kaur

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When legitimate credit card users are mistakenly labelled as fraudulent in numerous financial delated applications, there are numerous ethical problems. The innovative machine learning approach we have suggested in this research outperforms the current models and shows how to model a data set for credit card fraud detection while minimizing false positives. As a result, we advise using random forests as the best machine learning method for predicting and identifying credit card transaction fraud. The majority of victims of these fraudulent transactions were discovered to be credit card users over the age of 60, with a higher percentage of fraudulent transactions taking place between the specific hours.

Keywords: automated fraud detection, isolation forest method, local outlier factor, ML algorithm, credit card

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Authors: Kaleb Shiferaw, Berhanu Geberemedhin, Dereje Legesse

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Access to credit is often viewed as a key to transform semi-subsistence smallholders into market oriented producers. However, only a few studies have examined factors that affect farmers’ decision to allocate credit on farm activities in general and livestock production in particular. A trivariate probit model with double selection is employed to identify factors that affect farmers’ decision to allocate credit on livestock production using data collected from smallholder farmers in Ethiopia. After controlling for two sample selection bias – taking credit for the production season and decision to allocate credit on farm activities – land ownership and access to a livestock centered extension service are found to have a significant (p<0.001) effect on farmers decision to use credit for livestock production. The result showed farmers with large land holding, and access to a livestock centered extension services are more likely to utilize credit for livestock production. However since the effect of land ownership squared is negative the effect of land ownership for those who own a large plot of land lessens. The study highlights the fact that improving access to credit does not automatically translate into more productive households. Improving farmers’ access to credit should be followed by a focused extension services.

Keywords: livestock production, credit access, credit allocation, household decision, double sample selection

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Authors: Todor Nickolov Stoyanov

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These factors appear as a reason for difficulties in financing from programs for rural development of the European Union. Credit conditions for commercial banks are difficult to implement, and its interest rate is too high. One of the possibilities for short-term loans at preferential conditions for the small and medium-sized agricultural and forest owners is credit cooperative. After the changes, occurred in the country after 1990, the need to restore credit cooperatives raised. The purpose for the creation of credit cooperatives is to assist private agricultural and forest owners to take care for them, to assist in the expansion and strengthening of their farms, to increase the quality of life and to improve the local economy. It was found that: in Bulgaria there is a legal obstacle for credit cooperatives to expand their business in the deposit and lending sphere; private forest and agricultural owners need small loans to solve a small problem for a certain season; providing such loans is not attractive for banks, but it is extremely necessary for owners of small forests and lands; if a special law on credit cooperatives is adopted, as required by the Cooperatives Act, it will allow more local people to be members of such credit structures and receive the necessary loans. In conclusion, proposals to create conditions for the development of credit cooperatives in the country are made and positive results expected from the creation of credit cooperatives, are summarized.

Keywords: cooperatives, credit cooperatives, forestry, forest owners

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Authors: E. O. Fakoya, B. G. Abiona, W. O. Oyediran, A. M. Omoare

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This study examined the socio-economic effects of micro-credit on small scale poultry farmers’ livelihood in Ado Odo-Ota Local Government area of Ogun State. Purposive sampling method was used to select eighty (80) small scale poultry farmers that benefited in micro credit. Interview guide was used to obtain information on the respondents’ socio-economic characteristic, sources of micro-credit and the effects of micro-credit on their livelihood. The results revealed that most of the respondents (77.50 %) were males while half (40.00%) of the respondents were between the ages of 31-40 years. A high proportion (72.50%) of the respondents had formal education. The major sources of micro credit to small scale poultry farmers were cooperative society (47.50%) and personal savings (20.00%). The findings also revealed that micro-credit had positive effect on the assets and livelihoods of small scale poultry farmers’ livelihood. Results of t-test analysis showed a significant difference between the effects before and after micro-credit on small-scale poultry farmers’ Livelihood at p < 0.05. The study recommends that formal lending institution should be given necessary support by government to enable poultry farmers have access to credit facilities in the study area.

Keywords: micro-credit, effects, livelihood, poultry farmers, socio-economic, small scale

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Authors: Anne Gesare Timu, Megan Sheahan, Andrew Gache Mude, Rupsha Banerjee

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Women headed households are among the most vulnerable to negative climatic shocks and are often left poorer as a result. Credit provision has been recognized as one way of alleviating rural poverty and developing poor rural households’ resilience to shocks. Much has been documented about credit demand in small-holder agriculture settings in Kenya. However, little is known about demand for credit among pastoral women. This paper analyzes the determinants of demand for credit in the pastoral regions of Marsabit District of Northern Kenya. Using a five wave balanced panel data set of 820 households, a double hurdle model is employed to analyze if shocks, financial literacy and risk aversion affect credit demand among female and male headed households differently. The results show that borrowing goods on credit and monetary credit from informal market segments are the most common sources of credit in the study area. The impact of livestock loss and financial literacy on the decision to borrow and how much to borrow vary with gender. While the paper suggests that provision of credit is particularly valuable in the aftermath of a negative shock and more so for female-headed households, it also explores alternatives to the provision of credit where credit access is a constraint. It recommends further understanding of systems and institutions which could enhance access to credit, and particularly during times of stress, to enable households in the study area in particular and Northern Kenya in general to invest, engage in meaningful development and growth, and be resilient to persistent shocks.

Keywords: female headed households, pastoralism, rural financing, double hurdle model

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Authors: Drago Jakovcevic, Mihovil Andelinovic, Igor Husak

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The aim of this paper is to explore the economic circumstances in which the selective credit policy, the least used instrument of four types of instruments on disposal to central banks, should be used. The most significant example includes the use of selective credit policies in response to the emergence of the global financial crisis by the FED. Specifics of the potential use of selective credit policies as the instigator of economic growth in Croatia, a small open economy, are determined by high euroization of financial system, fixed exchange rate and long-term trend growth of external debt that is related to the need to maintain high levels of foreign reserves. In such conditions, the classic forms of selective credit policies are unsuitable for the introduction. Several alternative approaches to implement selective credit policies are examined in this paper. Also, thorough analysis of distribution of selective monetary policy loans among economic sectors in Croatia is conducted in order to minimize the risk of investing funds and maximize the return, in order to influence the GDP growth.

Keywords: global crisis, selective credit policy, small open economy, Croatia

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Authors: Showkat Ahmad Bhat, M. S. Bhatt

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The study evaluates the ex-post impact of institutional credit on agricultural output. It first examines the key factors that influence the accessibility of institutional credit by farm households. For quantitative analysis both program participant and non-participant respondents were drawn and cross-sectional survey data were collected from 412 households in Pulwama District of Jammu & Kashmir (India). Propensity Score Matching Method was employed to analyze the impact of the institutional credit on agricultural output. Results show that institutional credit has a positive and significant impact on the agricultural output measured in terms of farm income and crop productivity. To estimate the accessibility of credit, an examination of both demand side and supply side factors were carried out. The demand for credit was measured with respect to respondents who applied for credit. Supply side credit allocation measured in terms of the proportion of ‘credit amount’ farmers obtained. Logit and Two-limit Tobit Regression Models were used to investigate the determinants that influence the accessibility of formal credit for Demand for and supply of credit respectively. The estimated results suggested that the demand for credit is positively and significantly affected by the factors such as: age of the household head, formal education, membership, cash crop grown, farm size and saving account. All the variables were found significantly increasing the household’s likelihood to demand for and supply of credit from banks. However, the impact of these factors varies considerably across the credit markets. Factors which were found negatively and significantly influencing the accessibility of credit were: ‘square of the age’, household assets and rate of interest. The credit constraints analysis suggested that square of the age; household assets and rate of interest were the three most important factors that increased the probability of being constrained. The study finally discusses these results in detail and draws some recommendations.

Keywords: institutional credit, agriculture, propensity score matching logit model, Tobit model

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Authors: Sirus Sharifi, Arunima Haldar, S. V. D. Nageswara Rao

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The study attempts to analyze the impact of credit risk management practices of Indian scheduled commercial banks on their non-performing assets (NPAs). The data on credit risk practices was collected by administering a questionnaire to risk managers/executives at different banks. The data on NPAs (from 2012 to 2016) is sourced from Prowess, a database compiled by the Centre for Monitoring Indian Economy (CMIE). The model was estimated using cross-sectional regression method. As expected, the findings suggest that there is a negative relationship between credit risk management and NPA growth in Indian banks. The study has implications for Indian banks given the high level of losses, and the implementation of Basel III norms by the central bank, i.e. Reserve Bank of India (RBI). Evidence on credit risk management in Indian banks, and their relationship with non-performing assets held by them.

Keywords: credit risk, identification, Indian Banks, NPAs, ownership

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Authors: Babak Salekmahdy

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Microfinance is often seen as a means of repairing credit markets and unleashing the potential contribution of impoverished people who rely on self-employment. Since the 1990s, the microfinance industry has expanded rapidly, opening the path for additional kinds of social entrepreneurship and social investment. However, current data indicate relatively few average consumer effects, opposing pushback against microfinance. This research reconsiders microfinance statements, stressing the variety of data on impacts and the essential (but limited) role of reimbursements. The report finishes by explaining a shift in thinking: from microfinance as a strictly defined enterprise finance to microfinance as a more widely defined home finance. Microfinance, under this perspective, provides advantages by providing liquidity for various requirements rather than just by increasing income.

Keywords: microfinance, small business, economic development, credit markets

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Authors: Salima Smiti, Ines Gasmi, Makram Soui

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Credit risk is the most important issue for financial institutions. Its assessment becomes an important task used to predict defaulter customers and classify customers as good or bad payers. To this objective, numerous techniques have been applied for credit risk assessment. However, to our knowledge, several evaluation techniques are black-box models such as neural networks, SVM, etc. They generate applicants’ classes without any explanation. In this paper, we propose to assess credit risk using rules classification method. Our output is a set of rules which describe and explain the decision. To this end, we will compare seven classification algorithms (JRip, Decision Table, OneR, ZeroR, Fuzzy Rule, PART and Genetic programming (GP)) where the goal is to find the best rules satisfying many criteria: accuracy, sensitivity, and specificity. The obtained results confirm the efficiency of the GP algorithm for German and Australian datasets compared to other rule-based techniques to predict the credit risk.

Keywords: credit risk assessment, classification algorithms, data mining, rule extraction

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Authors: Sélima Baccar, Ephraim Clark

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This paper can be considered as a new perspective to analyse credit spreads. A comprehensive empirical analysis of conditional variance of credit spreads indices is performed using various GARCH models. Based on a comparison between traditional and asymmetric GARCH models with alternative functional forms of the conditional density, we intend to identify what macroeconomic and financial factors have driven daily changes in the US Dollar credit spreads in the period from January 2011 through January 2013. The results provide a strong interdependence between credit spreads and the explanatory factors related to the conditions of interest rates, the state of the stock market, the bond market liquidity and the exchange risk. The empirical findings support the use of asymmetric GARCH models. The AGARCH and GJR models outperform the traditional GARCH in credit spreads modelling. We show, also, that the leptokurtic Student-t assumption is better than the Gaussian distribution and improves the quality of the estimates, whatever the rating or maturity.

Keywords: corporate bonds, default risk, credit spreads, asymmetric garch models, student-t distribution

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Authors: Cheyne Kirkpatrick

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In an attempt to make the university intensive English program more worthwhile for students, many English language programs are redesigning curriculum to offer for-credit English for Academic Purposes classes, sometimes marketed as “bridge” courses. These programs are designed to be accredited to national language standards, provide communicative language learning, and give students the opportunity to simultaneously earn university language credit while becoming proficient in academic English. This presentation will discuss the curriculum design of one such program in the United States at a large private university that created its own for-credit “bridge” program. The planning, development, piloting, teaching, and challenges of designing this type of curriculum will be presented along with the aspects of accreditation, communicative language learning, and integration within various university programs. Attendees will learn about how such programs are created and what types of objectives and outcomes are included in American EAP classes.

Keywords: IEP, AEP, Curriculum, CEFR, University Credit, Bridge

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Authors: Heba Abdelmotaal

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This paper explores the impact of the extent of a company's environmental, social, and governance (ESG) disclosure on credit rating and capital structure. The analysis is based on a sample of 202 firms from the 350 FTSE firms over the period of 2008-2013. ESG disclosure score is measured using Proprietary Bloomberg score based on the extent of a company's Environmental, Social, and Governance (ESG) disclosure. The credit rating is measured by The QuiScore, which is a measure of the likelihood that a company will become bankrupt in the twelve months following the date of calculation. The Capital Structure is measured by long term debt ratio. Two hypotheses are test using panel data regression. The results suggested that the higher degree of ESG disclosure leads to better credit rating. There is significant negative relationship between ESG disclosure and the long term debit percentage. The paper includes implications for the transparency which is resulting of the ESG disclosure could support the Monitoring Function. The monitoring role of disclosure is the increasing in the transparency of the credit rating agencies, also it could affect on managers’ actions. This study provides empirical evidence on the material of ESG disclosure on credit ratings changes and the firms’ capital decision making.

Keywords: capital structure, credit rating agencies, ESG disclosure, panel data regression

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Authors: Ravipa Rojasavachai, Li Yang

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Working from home has become a global phenomenon after the coronavirus outbreak, and most employees have a choice to choose between working from home or the office. In this paper, we examine the demographics and socio-economics factors influencing individuals’ decision to choose working from home rather than the office before and after the coronavirus outbreak based on Australian household data. We find that all factors impact the working from home choice before the coronavirus outbreak, but the number of children turns to an uninfluenced factor on individuals’ choices after the outbreak. We also find that female employees have a higher probability of choosing to work from home after the coronavirus outbreak. This is because they have less concern for their career opportunities and higher wage premium of working from home due to the changing in cultural norms and advanced working from home technologies in companies after the coronavirus outbreak.

Keywords: work from home, telework, remote working, COVID-19, pandemic, wage

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Authors: Sirus Sharifi, Arunima Haldar, S. V. D. Nageswara Rao

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This research analyzes the effect of credit risk management practices of commercial banks in India and the relationship with their non-performing assets (NPAs). Required data on credit risk performance was collected through a survey questionnaire from top risk officers of 38 Indian banks. NPA data (period from 2012 to 2016) was collected from Prowess database compiled by the Centre for Monitoring Indian Economy (CMIE). The model was assessed utilizing cross sectional regression method. As expected, the results indicate a negative significant relationship between credit risk management in India banks and their NPA growth. The research has implications for banks given the high level of losses in India and other economies as well, and the implementation of Basel III standards by the central banks. This research would be an evidence on credit risk performance and its relationship with the level of non-performing assets (NPAs) in Indian banks.

Keywords: risk management, risk identification, banks, Non-Performing Assets (NPAs)

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Authors: Chuang-Chun Chiou

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Ageing population is a global trend; therefore the need of care service has been increasing dramatically. There are three basic forms of service delivered to the elderly: institution, community, and home. Particularly, the institutional service can be seen as an extension of medical service. The nursing home or so-called care home which is equipped with professional staff and facilities can provide a variety of service including rehabilitation service, short-term care, and long term care. Similar to hospital and other health care service, care home service do need to provide quality and cost-effective service to satisfy the dwellers. The main purpose of this paper is to show how lean thinking and service innovation can be applied to care home operation. The issues and key factors of implementing lean practice are discussed.

Keywords: lean, service improvement, SERVQUAL, care home service

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Authors: Siti Nor Amira Bt. Mohamad, Mohamad Yazis B. Ali Basah, Muhammad Ridhwan B. Ab. Aziz, Khairil Faizal B. Khairi, Mazlynda Bt. Md. Yusuf, Hisham B. Sabri

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The understanding of risk and the concept of it occurs associated in Islamic financing was well-known in the financial industry by the using of Profit-and-Loss Sharing (PLS). It was presently in any Islamic financial transactions in order to comply with shariah rules. However, the existence of risk in Murabahah contract of financing is an ability that the counterparty is unable to complete its obligations within the agreed terms. Therefore, it is called as credit or default risk. Credit risk occurs when the client fails to make timely payment after the bank makes complete delivery of assets. Thus, it affects the growth of the bank as the banking business is in no position to have appropriate measures to cover the risk. Therefore, the bank may impose penalty on the outstanding balance. This paper aims to highlight the credit risk determinant and issues surrounding in Islamic bank in Malaysia in terms of Murabahah financing and how to manage it by using the proper techniques. Finally, it explores the credit risk management concept that might solve the problems arise. The study found that the credit risk can be managed properly by improving the use of comprehensive reference checklist of business partners on their character and past performance as well as their comprehensive database. Besides that, prevention of credit risk can be done by using collateral as security against the risk and we also argue on the Shariah guidelines and procedures should be implement coherently by the banking business because so that the risk would be control by having an effective instrument for Islamic modes of financing.

Keywords: Islamic banking, credit risk, Murabahah financing, risk mitigation

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Authors: Imas Istiani

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The concept of home is usually closely related to the place of safety and security. For people who have travelled far and long, they long to be united with home to feel safe, secure and comfortable. However, for some people, especially for veterans, home cannot offer them those feelings, on the contrary, it can give them the sense of insecurity as well as guilty. Thus, its juxtaposed concept can also put home as an uncanny place that represses and haunt its occupant. As for veterans, 'survivor guilt' overpowers them in the way that it will be hard for them to embrace the comfort that home offers. In Home, Toni Morrison poignantly depicts Frank’s life upon returning from the war. Burdened with his traumatic experiences, Frank finds home full with terror, guilt, fear, grief, and loss. Using Dominick laCapra’s 'Trauma Theory,' the study finds that Frank works through his trauma by being able to distinguish between past and present so that he can overcome those repressed feelings. Aside from his inner healing power, Frank digests the process of working through with the help of home and community, as proposed by Evelyn Jaffe Schreiber claiming that community can help survivors to heal from traumatic experiences. Thus, Home has two juxtaposed functions; both as traumatizing and healing place.

Keywords: trauma, healing, home, trauma theory

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Authors: S. Ibrahim, Bashir Umar

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Agricultural credit is very important for sustainable agricultural development to be achieved in any country of the world. Rural credit has proven to be a powerful instrument against poverty reduction and development in rural area. Agricultural credit enhances productivity and promotes standard of living by breaking vicious cycle of poverty of small scale farmers. This study examined access to credit among rural farmers in Giwa local government area of Kaduna state. Two stages sampling procedure was employed to select forty-two (42) respondents for the study. Primary data were collected using structured questionnaire with the help of well-trained enumerators. Data were analyzed using simple descriptive statistics. The results revealed that farmers were predominantly male (57.1%) and most (54.7%), were married with one level of education or another (66.5.%). Majority of the households’ head were between the ages of 31 to 50. majority of the farmers (68.2%) had more than 2ha of farmlands with at least 5 years of farming experience and an annual farm income of N 61,000 to 100,000 (61.9%). The Various sources of credit by the farmers in the study area were commercial banks (38.1%), Co-operative banks (47.6%), Development banks (14.2%) (formal) and Relatives (26.1%), Personal Savings (Adashi scheme) (52.3%), Moneylenders (21.4%) (informal). As regard to the amount of credit obtained by the farmers 38.1% received N 50,000-100,000, 50 % obtained N 100,001-500,000 while 11.9% obtained N 500,001-1,000,000. High interest Inadequate collateral, Complicated Procedures, lack of guarantor were the major constrains encountered by the farmers in accessing loans. The study therefore recommends that Rural farmers should be encouraged to form credit and thrift cooperative societies from which they can access much cheaper credits, Moreover, to ensure that any credit obtained may be manageable for the farmers, financial institutions should provide loans with low interest rates and government and non-governmental organizations should simplify procedures associated with accessing loans.

Keywords: analysis, access, credit, farmers

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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: Abhinav Singh

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Point Of Sale (POS) malwares have been stealing the limelight this year. They have been the elemental factor in some of the biggest breaches uncovered in past couple of years. Some of them include • Target: A Retail Giant reported close to 40 million credit card data being stolen • Home Depot : A home product Retailer reported breach of close to 50 million credit records • Kmart: A US retailer recently announced breach of 800 thousand credit card details. Alone in 2014, there have been reports of over 15 major breaches of payment systems around the globe. Memory scrapping malwares infecting the point of sale devices have been the lethal weapon used in these attacks. These malwares are capable of reading the payment information from the payment device memory before they are being encrypted. Later on these malwares send the stolen details to its parent server. These malwares are capable of recording all the critical payment information like the card number, security number, owner etc. All these information are delivered in raw format. This Talk will cover the aspects of what happens after these details have been sent to the malware authors. The entire ecosystem of credit card frauds can be broadly classified into these three steps: • Purchase of raw details and dumps • Converting them to plastic cash/cards • Shop! Shop! Shop! The focus of this talk will be on the above mentioned points and how they form an organized network of cyber-crime. The first step involves buying and selling of the stolen details. The key point to emphasize are : • How is this raw information been sold in the underground market • The buyer and seller anatomy • Building your shopping cart and preferences • The importance of reputation and vouches • Customer support and replace/refunds These are some of the key points that will be discussed. But the story doesn’t end here. As of now the buyer only has the raw card information. How will this raw information be converted to plastic cash? Now comes in picture the second part of this underground economy where-in these raw details are converted into actual cards. There are well organized services running underground that can help you in converting these details into plastic cards. We will discuss about this technique in detail. At last, the final step involves shopping with the stolen cards. The cards generated with the stolen details can be easily used to swipe-and-pay for purchased goods at different retail shops. Usually these purchases are of expensive items that have good resale value. Apart from using the cards at stores, there are underground services that lets you deliver online orders to their dummy addresses. Once the package is received it will be delivered to the original buyer. These services charge based on the value of item that is being delivered. The overall underground ecosystem of credit card fraud works in a bulletproof way and it involves people working in close groups and making heavy profits. This is a brief summary of what I plan to present at the talk. I have done an extensive research and have collected good deal of material to present as samples. Some of them include: • List of underground forums • Credit card dumps • IRC chats among these groups • Personal chat with big card sellers • Inside view of these forum owners. The talk will be concluded by throwing light on how these breaches are being tracked during investigation. How are credit card breaches tracked down and what steps can financial institutions can build an incidence response over it.

Keywords: POS mawalre, credit card frauds, enterprise security, underground ecosystem

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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

Abstract:

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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