Juan Lara-Rubio

Improving the management of microfinance institutions by using credit scoring models based on Statistical Learning techniques

A wide range of supervised classification algorithms have been successfully applied for credit scoring in non-microfinance environments according to recent literature. However, credit scoring in the microfinance industry is a relatively recent application, and current research is based, to the best of our knowledge, on classical statistical methods. This lack is surprising since the implementation of credit scoring based on supervised classification algorithms should contribute towards the efficiency of microfinance institutions, thereby improving their competitiveness in an increasingly constrained environment. This paper explores an extensive list of Statistical Learning techniques as microfinance credit scoring tools from an empirical viewpoint. A data set of microcredits belonging to a Peruvian Microfinance Institution is considered, and the following models are applied to decide between default and non-default credits: linear and quadratic discriminant analysis, logistic regression, multilayer perceptron, support vector machines, classification trees, and ensemble methods based on bagging and boosting algorithm. The obtained results suggest the use of a multilayer perceptron trained in the R statistical system with a second order algorithm. Moreover, our findings show that, with the implementation of this MLP-based model, the MFIs@? misclassification costs could be reduced to 13.7% with respect to the application of other classic models.

Loan price modelling for local governments using risk premium analysis

Previous studies have highlighted the question of government loan interest as one of great current importance. Government borrowing levels are high, and reducing interest payments would generate savings to meet other spending needs and/or to lower taxation, thus supporting the sustainability of public finances. However, no previous study has presented a method for a local government to calculate its own credit risk and thus be in a position to negotiate lower interest rates on its borrowing. This article defines a financial model that enables local governments to estimate the interest rate payable on a bank loan, based on their credit risk premium, in accordance with the Basel II rules and the findings of our empirical study of large local governments.

Analysing credit risk in large local governments: an empirical study in Spain

ABSTRACT In governments throughout the world, bank lending excesses, solvency issues and worsening credit ratings have all contributed to raising risk premiums and impeding access to credit, thus provoking a major financial problem in the public sector. Accordingly, tax authorities and regulators need to analyse the causes of public sector bank debt, doing so through the joint study of idiosyncratic and systematic variables, an area that has been neglected in previous research. This paper examines idiosyncratic and systematic factors that may influence local government credit risk through an empirical study of the performance of 148 large Spanish municipalities during 2006–2011. We identify individual factors relevant to the probability of local government default (such as dependent population, per capita income and debt composition) and also determinants associated with macroeconomic developments, such as gross domestic product and the risk premium.

What can increase the default risk in local governments

Concern has been expressed by international organisations and in previous studies about the financial situation of local governments, and the question of debt has been identified as a crucial element in efforts to overcome the current financial crisis. However, the variables that can affect the financial soundness of these governments have not been sufficiently studied, despite their direct relation to the credit risk premium. In this article, we aim to identify risk factors for default by local governments, and provide useful information to municipal financial managers. We conducted an empirical study of 148 Spanish municipalities and analysed data from four years, applying a random effects logistic regression model. Our findings reveal that a lower population density, less dependent population, falling levels of per capita income and the presence of progressive local government are all risk factors for default by local governments. Furthermore, our findings indicate that the general financing structure variable and debt composition and maturity variable do influence the risk of default by local governments. Points for practitioners The findings of this article can provide useful information for managers and politicians responsible for the financial management of local governments, in particular, by enabling them to better understand the risk premiums assigned by banks. Specifically, by identifying the risk factors for default, this article highlights the warning signs of this risk, so that suitable arguments may be expressed in negotiating loan repayment schedules and interest rates, and in designing financial viability and restructuring plans.

Promoting Entrepreneurship at the Base of the Social Pyramid via Pricing Systems: A case Study

Historically, microfinance institutions MFIs have played a significant social role by helping people at the base of the socio-economic pyramid escape from social exclusion through the creation of microenterprises. However, international banks have recently started competing in the microfinance sector. In this adverse environment, MFI management tools should be more innovative and technologically advanced to increase efficiency, solvency and profitability and to compete with commercial banks on equal terms. This study therefore strives to develop a credit-risk management tool based on a multilayer perceptron MLP credit-scoring model for a Peruvian MFI, and to calculate the capital requirements and microcredit pricing on both internal ratings-based IRB and standardized approaches, analysing the impact of these models on the management of the MFI. Our findings show that the implementation of an IRB approach with default probabilities obtained from an MLP credit-scoring model produces the best benefit by the MFIs in terms of higher accuracy reduction of misclassification costs by 13.78%, lower capital requirements in the range of 8.5-78% and the best risk-adjusted interest rates. Furthermore, with the establishment of interest rates adjusted to the real risk of each client, MFIs are fairer and more socially engaged by preventing economically viable low-risk projects from becoming unviable due to excessive interest rates. This leads to the creation of more small businesses by people from the base of the socio-economic pyramid and greater economic development and social cohesion. The IRB model should therefore be implemented to improve MFI solvency, profitability, efficiency, survival, management and social performance.