Author: Aruppillai Thayaparan
Publisher: GRIN Verlag
ISBN: 3346085368
Category : Business & Economics
Languages : en
Pages : 52
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Book Description
Document from the year 2019 in the subject Business economics - Investment and Finance, , course: ECONOMICS, language: English, abstract: The main objective of the study is to identify the borrower characteristics that discriminate them into defaulters and non- defaulters and examine the determinants of loan repayment and their credit worthiness in Microfinance institutions in Vavuniya district in Sri Lanka. In line with above general objective, this study has the following specific objectives: To identify the borrower characters those classify them into defaulters and non-defaulters in the study area. To evaluate the impact of major demographic characters such as age, gender, levels of education, civil status and family members of the borrowers that impact on their repayment performance and credit worthiness. To investigate how the farming characters like income, farm size, ownership of land, farming experience and availability of non-farm income as well as farmers' attributes such as purposes of loan, crop failure, weather conditions and knowledge about loans affect loan repayment and discriminate the borrowers into two groups in the study area. Financial institutions and banks have major role in financial sector as well as rural sector of an economy in terms of providing loans to the rural community in developing countries like Sri Lanka. The borrowers especially, farmers are able to get the loans from the microfinance institutions to improve their living standard through agricultural activities and generate their income. Even the borrowers have chances to receive the loans, the microfinance institutions and banks are facing the problems to recover the loans from the borrowers. Thus, default rate among the borrowers has been increasing over time which is the difficult task to manage the banks and financial institutions. There are a number of many factors particularly demographic and farming characters that affect the loan repayment rates. There has not been any empirical research conducted regarding to repayment performance among the borrowers who get the loans from SANASA Thrift, Credit and Cooperative Society (TCCS) banks in Vavuniya district. Therefore, this study tries to provide the relevant information for a better understanding on the determinants of loan repayment performance of the borrowers and the information will be useful for policy makers, other lending institutions and stakeholders for their future decision making on granting the loans for their clients.
Author: Li Gan
Publisher: Intl Food Policy Res Inst
ISBN:
Category : Social Science
Languages : en
Pages : 44
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Book Description
Group lending has been widely adopted in the past thirty years by many microfinance institutions as a means to mitigate information asymmetries when delivering credit to the poor. This paper proposes an empirical method to address the potential omitted-variable problem resulting from unobserved group types when modeling the repayment behavior of group members. We estimate the model using a rich dataset from a group-lending program in India. The estimation results support our model specification and show the advantages of relying on a type-varying method when analyzing the probability of default of group members. In particular, our model helps to better understand the factors driving repayment behavior, which may differ across group types, and shows a higher predictive power than standard single-agent choice models.
Author: Nader Damerji
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
60% of Americans are classified by the Fair, Isaac and Company (FICO) score as subprime consumers, indicating that many Americans are forced to seek alternative ways to obtain credit. (Marte, 2015) Payday loans and short-term loan lenders are often one of the sources that subprime users turn to for credit. DecisionLogic aims to provide lenders with additional information to be used in lending decision making. An analysis of DecisionLogic's transaction database was executed to identify key variables that are indicative of borrower's loan repayment behavior based on borrower's 90-day bank account statements. Data mining of DecisionLogic's database consisting of 322 Million bank account statements was performed to determine descriptive statistics and market share. Hypothesis testing of the results was conducted to compare DecisionLogic's Lender's customer's spending habits to the United States consumer expenditures. In addition, variables that would indicate borrower's repayment behavior were identified and tested for significance by logistic regression and discriminant analysis against current lender output data. Several variables were found to be significantly related to borrowers' loan repayment behavior. Among these variables are retained earnings, credit card payments and loan payments. In addition, variables that considered frequency of purchase were better at predicting defaulters compared to using the same variables as a percentage of credit.
Author: Raluca Caplescu
Publisher:
ISBN:
Category :
Languages : en
Pages : 14
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Book Description
Recent increase in P2P lending prompted for development of models to separate good and bad clients to mitigate risks both for lenders and for the platforms. The rapidly increasing body of literature provides several comparisons between various models. Among the most frequently employed ones are logistic regression, SVM, neural networks and decision tree-based ones. Among them, logistic regression has proved to be a strong candidate both because its good performance and due to its high explainability. The present paper aims to compare four pairs of models (for imbalanced and under-sampled data) meant to predict charged off clients by optimizing f1 score. We found that, if the data is balanced, Logistic Regression, both simple and with Stochastic Gradient Descent, outperforms LightGBM and K-Nearest Neighbors in optimizing f1 score. We chose this metric as it provides balance between the interests of the lenders and those of the platform. Loan term, DTI and number of accounts were found to be important positively related predictors of risk of charge off. At the other end of the spectrum, by far the strongest impact on charge off probability is that of the FICO score. The final number of features retained by the two models differs very much, because, although both models use Lasso for feature selection, Stochastic Gradient Descent Logistic Regression uses a stronger regularization. The analysis was performed using Python (numpy, pandas, sklearn and imblearn).
Author: Li Gan
Publisher:
ISBN:
Category :
Languages : en
Pages : 50
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Book Description
This paper proposes and implements a mixture model to account for the unobserved group heterogeneity when modeling repayment behavior in group lending. We discuss the model properties and identification. We estimate the model using a rich dataset from a group lending program in India. The estimation results support the existence of two different group types: “responsible” and “irresponsible” groups. We find that the effects of the factors driving the repayment behavior differ across types. The model also shows a higher predictive performance than standard probabilistic models, particularly in identifying potential defaulters. We provide evidence supporting the robustness of the estimations.
Author: Lyn Thomas
Publisher: SIAM
ISBN: 1611974550
Category : Business & Economics
Languages : en
Pages : 380
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Book Description
Credit Scoring and Its Applications?is recognized as the bible of credit scoring. It contains a comprehensive review of the objectives, methods, and practical implementation of credit and behavioral scoring. The authors review principles of the statistical and operations research methods used in building scorecards, as well as the advantages and disadvantages of each approach. The book contains a description of practical problems encountered in building, using, and monitoring scorecards and examines some of the country-specific issues in bankruptcy, equal opportunities, and privacy legislation. It contains a discussion of economic theories of consumers' use of credit, and readers will gain an understanding of what lending institutions seek to achieve by using credit scoring and the changes in their objectives.? New to the second edition are lessons that can be learned for operations research model building from the global financial crisis, current applications of scoring, discussions on the Basel Accords and their requirements for scoring, new methods for scorecard building and new expanded sections on ways of measuring scorecard performance. And survival analysis for credit scoring. Other unique features include methods of monitoring scorecards and deciding when to update them, as well as different applications of scoring, including direct marketing, profit scoring, tax inspection, prisoner release, and payment of fines.?
Author: Majid Bazarbash
Publisher: International Monetary Fund
ISBN: 1498314422
Category : Business & Economics
Languages : en
Pages : 34
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Book Description
Recent advances in digital technology and big data have allowed FinTech (financial technology) lending to emerge as a potentially promising solution to reduce the cost of credit and increase financial inclusion. However, machine learning (ML) methods that lie at the heart of FinTech credit have remained largely a black box for the nontechnical audience. This paper contributes to the literature by discussing potential strengths and weaknesses of ML-based credit assessment through (1) presenting core ideas and the most common techniques in ML for the nontechnical audience; and (2) discussing the fundamental challenges in credit risk analysis. FinTech credit has the potential to enhance financial inclusion and outperform traditional credit scoring by (1) leveraging nontraditional data sources to improve the assessment of the borrower’s track record; (2) appraising collateral value; (3) forecasting income prospects; and (4) predicting changes in general conditions. However, because of the central role of data in ML-based analysis, data relevance should be ensured, especially in situations when a deep structural change occurs, when borrowers could counterfeit certain indicators, and when agency problems arising from information asymmetry could not be resolved. To avoid digital financial exclusion and redlining, variables that trigger discrimination should not be used to assess credit rating.
Author: Xavier Gine
Publisher: World Bank Publications
ISBN: 0609181742
Category : Bank Policy
Languages : en
Pages : 38
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Book Description
Group liability is often portrayed as the key innovation that led to the explosion of the microcredit movement, which started with the Grameen Bank in the 1970s and continues on today with hundreds of institutions around the world. Group lending claims to improve repayment rates and lower transaction costs when lending to the poor by providing incentives for peers to screen, monitor, and enforce each other's loans. However, some argue that group liability creates excessive pressure and discourages good clients from borrowing, jeopardizing both growth and sustainability. Therefore, it remains unclear whether group liability improves the lender's overall profitability and the poor's access to financial markets. The authors worked with a bank in the Philippines to conduct a field experiment to examine these issues. They randomly assigned half of the 169 pre-existing group liability 'centers' of approximately twenty women to individual-liability centers (treatment) and kept the other half as-is with group liability (control). We find that the conversion to individual liability does not affect the repayment rate, and leads to higher growth in center size by attracting new clients.
Author: Christopher Woodruff, David McKenzie, Suresh de Mel
Publisher: World Bank Publications
ISBN:
Category :
Languages : en
Pages : 37
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Book Description
Abstract: Small and informal firms account for a large share of employment in developing countries. The rapid expansion of microfinance services is based on the belief that these firms have productive investment opportunities and can enjoy high returns to capital if given the opportunity. However, measuring the return to capital is complicated by unobserved factors such as entrepreneurial ability and demand shocks, which are likely to be correlated with capital stock. The authors use a randomized experiment to overcome this problem and to measure the return to capital for the average microenterprise in their sample, regardless of whether they apply for credit. They accomplish this by providing cash and equipment grants to small firms in Sri Lanka, and measuring the increase in profits arising from this exogenous (positive) shock to capital stock. After controlling for possible spillover effects, the authors find the average real return to capital to be 5.7 percent a month, substantially higher than the market interest rate. They then examine the heterogeneity of treatment effects to explore whether missing credit markets or missing insurance markets are the most likely cause of the high returns. Returns are found to vary with entrepreneurial ability and with measures of other sources of cash within the household, but not to vary with risk aversion or uncertainty.
Author: Shigeyuki Hamori
Publisher: MDPI
ISBN: 3038977063
Category : Business & Economics
Languages : en
Pages : 276
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Book Description
There is no denying the role of empirical research in finance and the remarkable progress of empirical techniques in this research field. This Special Issue focuses on the broad topic of “Empirical Finance” and includes novel empirical research associated with financial data. One example includes the application of novel empirical techniques, such as machine learning, data mining, wavelet transform, copula analysis, and TV-VAR, to financial data. The Special Issue includes contributions on empirical finance, such as algorithmic trading, market efficiency, market microstructure, portfolio theory and asset allocation, asset pricing models, liquidity risk premium, currency crisis, return predictability, and volatility modeling.
