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

Cultural Sustainability in University Students’ Flamenco Music Event Attendance: A Neural Networks Approach

University students consume live music; however, almost 40% declare that they have never attended a flamenco show, an intangible heritage of humankind. Numerous studies have shown that cultural capital and socioeconomic profile, among other factors, are variables that influence cultural consumption, and therefore, cultural sustainability. Considering the relationship between several variables, this paper pursues a double objective. On the one hand, identifying the factors that influence attendance at flamenco shows, and on the other, proposing a predictive model that quantifies the likelihood of an individual attending a flamenco show. To this end, we analyse flamenco consumption by means of a survey conducted on 452 university students, using Multilayer Perceptrom (a non-parametric model), a methodology based on an artificial neural network. Our results confirm the importance of cultural capital, as well as personal and external factors, among other. The findings of this research work are of potential interest for management and planning of cultural events, as well as to promote cultural sustainability

Proposta de um modelo ensemble para credit scoring / Proposal for an ensemble model for credit scoring

Os modelos de Credit Scoring foram desenvolvidos com a finalidade de identificar bons e maus pagadores de financiamentos, de acordo com dados cadastrais que definem seu perfil.  Em se tratando de análise de crédito, será utilizado técnicas de aprendizado supervisionado, no qual o objetivo deste estudo envolve a contrução de um modelo estatístico para classificar um cliente entre bom e mau pagador, tomando com base variáveis que descrevem o perfil do mesmo.  Os dados de análise de crédito são de difícil acesso, para contornar esse problema foi utilizado a base de dados de acesso livre German Credit Data, adquiridos pelo site UC Irvine Machine Learning Repository. A metodologia adotada consiste em dividir o conjunto de dados em 80% para treinamento e 20% para teste. Destes 80%, retiram-se 10 amostras aleatórias com reposição de 70% (700 observações). Em cada amostra foi aplicado os classificadores: Naive Bayes, SVM, Regressão Logística, KNN e Árvore de decisão. Desta forma, depois de treinar os classificadores foram aplicados no conjunto de teste, e por sua vez, combinados por votos, gerando a classificação desejada para cada amostra, posteriormente, combinados por voto para gerar a classificação final. A metodologia proposta obteve resultados satisfatórios em comparação com os classificadores mais utilizados na literatura, incluindo métodos de combinação como Bagging, alcançando melhores desempenhos em acurácia e especificidade, e, com um dos menores erros para o falso positivo, ou seja, quando se classifica um cliente como  bom sendo que na realidade é mal pagador

Classification Algorithms in Financial Application: Credit Risk Analysis on Legal Entities

This research aims at analyzing bank credit of legal entity (in non-default, default and temporarily default), for the purpose of assisting the decision made by the analyst of this area. For that, we used Artificial Neural Networks (ANNs), more specifically, the Multilayer Perceptron (MLP) and the Radial Basis Functions (RBF) and, also, the statistical model of Logistic Regression (LR). For the implementation of the ANNs and LR, the softwares MATLAB and SPSS were used, respectively. For the simulations developed 5.432 data with 15 attributes were collected by the experts of the institution bank (called “XYZ”). The results show that the default clients are easily identifiable, but for the nondelinquent clients and for the temporarily defaulters, the techniques had greater difficulty in the discrimination, suggesting that they are no so discriminants. The main contributions of this work are: the analysis of three classes of clients (non-default, default and temporarily default), rather than just two (non-default and default) as is usually done; the coding of variables (attributes) of the company XYZ aiming to maximize the accuracy of the techniques and the use of the one-against all method, little used by the researchers of this research area. This work presents new insights towards research over Credit Risk Assessment showing other possibilities of client classification and codification, allowing different types of studies to take place

La evaluación del riesgo de crédito en las instituciones de microfinanzas

This paper reviews the empirical research focused on credit risk assessment in microfinance institutions (MFIs), particularly identifying those related to Latin America. Since the pioneering work of Vigano (1993), literature has spread over the last two decades, covering a significant number of countries and with the main objective of assessing credit risk. First, this work focuses on identifying the use of credit scoring techniques in the literature to assess the risk of microcredit incurring some type of costly delay. In this way, the MFI could establish measures to mitigate and be more efficient. The theoretical analysis of these investigations shows the majority use of parametric techniques. However, more recent research finds that non-parametric techniques have a greater predictive power of non-compliance by microcredit clients. Second, this paper identifies the determinants of default risk analyzed in previous works. The evidence shows the importance of qualitative information about the borrower, the business and the loan, as well as the use of unstructured data.Este trabajo realiza una revisión de las investigaciones empíricas focalizadas en la evaluación del riesgo de crédito en las instituciones de microfinanzas (IMFs), identificando de forma particular aquellos relativos a América Latina. Desde el trabajo pionero de Vigano (1993), la literatura se ha extendido en las últimas dos décadas, abarcando un número relevante de países y con el objetivo principal de evaluar el riesgo de crédito. Así, en primer lugar, este trabajo se centra en identificar el uso de las técnicas de credit scoring en la literatura para evaluar el riesgo de que los microcréditos incurran en algún tipo de atraso costoso. De este modo, la IMF podría establecer medidas orientadas a mitigarlo y ser más eficiente. El análisis teórico de estas investigaciones muestra la utilización mayoritaria de técnicas paramétricas. Sin embargo, las investigaciones más recientes encuentran que las técnicas no paramétricas tienen un mayor poder predictivo del incumplimiento por parte de los clientes de microcréditos. En segundo lugar, este trabajo identifica los determinantes del riesgo de impago analizados en trabajos previos. La evidencia muestra la importancia de la información cualitativa sobre el prestatario, el negocio y el préstamo, y también el uso de datos no estructurados

La evaluación del riesgo de crédito en las instituciones de microfinanzas

This paper reviews the empirical research focused on credit risk assessment in microfinance institutions (MFIs), particularly identifying those related to Latin America. Since the pioneering work of Vigano (1993), literature has spread over the last two decades, covering a significant number of countries and with the main objective of assessing credit risk. First, this work focuses on identifying the use of credit scoring techniques in the literature to assess the risk of microcredit incurring some type of costly delay. In this way, the MFI could establish measures to mitigate and be more efficient. The theoretical analysis of these investigations shows the majority use of parametric techniques. However, more recent research finds that non-parametric techniques have a greater predictive power of non-compliance by microcredit clients. Second, this paper identifies the determinants of default risk analyzed in previous works. The evidence shows the importance of qualitative information about the borrower, the business and the loan, as well as the use of unstructured data.Este trabajo realiza una revisión de las investigaciones empíricas focalizadas en la evaluación del riesgo de crédito en las instituciones de microfinanzas (IMFs), identificando de forma particular aquellos relativos a América Latina. Desde el trabajo pionero de Vigano (1993), la literatura se ha extendido en las últimas dos décadas, abarcando un número relevante de países y con el objetivo principal de evaluar el riesgo de crédito. Así, en primer lugar, este trabajo se centra en identificar el uso de las técnicas de credit scoring en la literatura para evaluar el riesgo de que los microcréditos incurran en algún tipo de atraso costoso. De este modo, la IMF podría establecer medidas orientadas a mitigarlo y ser más eficiente. El análisis teórico de estas investigaciones muestra la utilización mayoritaria de técnicas paramétricas. Sin embargo, las investigaciones más recientes encuentran que las técnicas no paramétricas tienen un mayor poder predictivo del incumplimiento por parte de los clientes de microcréditos. En segundo lugar, este trabajo identifica los determinantes del riesgo de impago analizados en trabajos previos. La evidencia muestra la importancia de la información cualitativa sobre el prestatario, el negocio y el préstamo, y también el uso de datos no estructurados

The Impact of Social Media on the Performance of Microfinance Institutions in Developing Countries:A Quantitative Approach

PurposeOver the last few decades, microfinance industry is argued to have played a constructive role in alleviating poverty level and providing the underprivileged with access to financial services. Statistics from the World Bank reveal that, currently, only 4% of the underprivileged have been served out of the 3 billion+ potential clients. Such results are due to several claims, particularly the operational and financial challenges faced by microfinance institutions (MFIs) in the constant flux inviting more attentions towards its performance. While explicit attention is given by many researchers towards mobile banking and information and communication technology (ICT) in improving the MFIs’ performance, the study on how social media, as a rapidly growing online phenomenon, can impact on the MFIs’ performance remains scarce. As such, this study aims to investigate this impact based on four dimensional performance indicators: efficiency, financial sustainability, portfolio quality and outreach.Design/methodology/approachA model is proposed and tested to ascertain the relationship between social media applications and organisational performance. In so doing, web-based questionnaires have been used to collect data from MFI employees in developing countries. Results reveal a significant influence of the social media over the MFIs’ performance, offering valuable insights into both researchers and practitioners in the domain of microfinance, as well as social media—conforming that the adoption of social media as marketing, advertising and communication tools may significantly improve the MFIs’ performance.FindingsThe results demonstrate that there is a positive and significant impact of social media use within microfinance on the key indicators of MFIs. They also show that the highest impact of social media usage within the microfinance is on the portfolio quality. In addition, it was found that marketing and advertising; communication and sales and distribution are the main areas where social media is able to support while social networking websites are the most popular platforms employed in MFIs.Originality/valueThis study adds to the existing literature few theoretical and practical aspects. First, this study developed a model for assessing the value of social media as a new phenomenon within this type of organisation. Second, it offers microfinance sponsors, managers and policy makers with a frame of reference to understand what social media platform can be deployed for each purpose. Third, with the identification of the main MFIs’ performance indicators, this research provided a reference of performance measurement guide for microfinance industry when assessing different technological employment

An academic review: applications of data mining techniques in finance industry

With the development of Internet techniques, data volumes are doubling every two years, faster than predicted by Moore’s Law. Big Data Analytics becomes particularly important for enterprise business. Modern computational technologies will provide effective tools to help understand hugely accumulated data and leverage this information to get insights into the finance industry. In order to get actionable insights into the business, data has become most valuable asset of financial organisations, as there are no physical products in finance industry to manufacture. This is where data mining techniques come to their rescue by allowing access to the right information at the right time. These techniques are used by the finance industry in various areas such as fraud detection, intelligent forecasting, credit rating, loan management, customer profiling, money laundering, marketing and prediction of price movements to name a few. This work aims to survey the research on data mining techniques applied to the finance industry from 2010 to 2015.The review finds that Stock prediction and Credit rating have received most attention of researchers, compared to Loan prediction, Money Laundering and Time Series prediction. Due to the dynamics, uncertainty and variety of data, nonlinear mapping techniques have been deeply studied than linear techniques. Also it has been proved that hybrid methods are more accurate in prediction, closely followed by Neural Network technique. This survey could provide a clue of applications of data mining techniques for finance industry, and a summary of methodologies for researchers in this area. Especially, it could provide a good vision of Data Mining Techniques in computational finance for beginners who want to work in the field of computational finance

The impact of social media on the performance of microfinance institutions in developing countries: a quantitative approach

Purpose: Over the last few decades, microfinance industry is argued to have played a constructive role in alleviating poverty level and providing the underprivileged with access to financial services. Statistics from the World Bank reveal that, currently, only 4% of the underprivileged have been served out of the 3 billion+ potential clients. Such results are due to several claims, particularly the operational and financial challenges faced by microfinance institutions (MFIs) in the constant flux inviting more attentions towards its performance. While explicit attention is given by many researchers towards mobile banking and information and communication technology (ICT) in improving the MFIs’ performance, the study on how social media, as a rapidly growing online phenomenon, can impact on the MFIs’ performance remains scarce. As such, this study aims to investigate this impact based on four dimensional performance indicators: efficiency, financial sustainability, portfolio quality and outreach. Design/methodology/approach: A model is proposed and tested to ascertain the relationship between social media applications and organisational performance. In so doing, web-based questionnaires have been used to collect data from MFI employees in developing countries. Results reveal a significant influence of the social media over the MFIs’ performance, offering valuable insights into both researchers and practitioners in the domain of microfinance, as well as social media—conforming that the adoption of social media as marketing, advertising and communication tools may significantly improve the MFIs’ performance. Findings: The results demonstrate that there is a positive and significant impact of social media use within microfinance on the key indicators of MFIs. They also show that the highest impact of social media usage within the microfinance is on the portfolio quality. In addition, it was found that marketing and advertising; communication and sales and distribution are the main areas where social media is able to support while social networking websites are the most popular platforms employed in MFIs. Originality/value: This study adds to the existing literature few theoretical and practical aspects. First, this study developed a model for assessing the value of social media as a new phenomenon within this type of organisation. Second, it offers microfinance sponsors, managers and policy makers with a frame of reference to understand what social media platform can be deployed for each purpose. Third, with the identification of the main MFIs’ performance indicators, this research provided a reference of performance measurement guide for microfinance industry when assessing different technological employment