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

Abstract

Summary 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.

Analysis of default risk in microfinance institutions under the Basel III framework

“…As explained in the first section, both the recommendations of international bodies and the conclusions of previous research (Navarro et al, 2020;Lara-Rubio et al, 2017;EU, 2015;IMF, 2015;World Bank, 2014;Elgin & Uras, 2013;Castro, 2013;Schularick & Taylor, 2012) have highlighted the urgent current need to incorporate systemic factors into the analysis of default risk, thus improving on the traditional approach based on idiosyncratic factors alone. In this respect, variables such as GDP (real gross domestic product) and COLCAP (the main stock market index of the Colombia Stock Exchange) may exert a direct influence on PD (Shahriar et al, 2016;Shahriar & Garg, 2017;Zafra-Gómez, López-Hernández, & Hernández-Bastida, 2009).…”

Predicting SME loan delinquencies during recession using accounting data and SME characteristics: The case of Greece

“…Additionally, Lara‐Rubio et al . () compared an MLP credit‐scoring model and an LR model under the Basel II internal ratings‐based (IRB) recommendations, for a Peruvian microfinance institution. They showed that the MLP‐IRB model performed better than the LR‐IRB model with important implications for microfinance institutions in terms of improving their profitability and efficiency.…”

“…As the crisis escalates, the SVM model performs better than the other models, followed by the DT and then the MLP. Our study contributes to the existing credit‐scoring literature for SMEs' delinquencies (Wu & Wang, ; Bensic et al ., ; Abdou, El‐Masry, & Pointon, ; Crook, Edelman, & Lyn, ; Mileris & Boguslauskas, ; Louzada, Ferreira‐Silva, & Diniz, ; Lara‐Rubio et al ., ; Sun & Vasarhelyi, ) with important implications for bank management and policy‐makers. The rest of the paper is structured as follows.…”

Developing a Comprehensive Framework for Crowd Funding Factors by Using the Hexagon Technique

“…The emergence of Web 2.0 and social networks in business models and financing methods has led to dramatic changes in start-up businesses and innovative entrepreneurs. One of these changes has occurred through the emergence of crowd funding in the field of financing [8]. Crowd funding is rooted in crowdsourcing and has been extracted from outsourcing.…”

“…Crowd funding can be considered to have originated from three scientific fields. The first is financial management, which is a microfinance entity [2,8,20]. According to the definition provided in Britannica Encyclopedia, microfinance is a means of extending credit, usually in the form of small loans with no collateral, to nontraditional borrowers such as the poor in rural or undeveloped areas [21].…”