Creditworthiness using social capital: a theoretical framework - Shimizu, Roberto Almeida (2023)
It is well known that in small communities, social capital plays a very important role in allowing economic transactions, especially in solidary lending arrangements, such as Rotating Savings and Credit Associations (ROSCAs), peer-to-peer loans among family and friends, and so on. More recently there were lots of academic papers analyzing the performance of peer-to-peer credit in fintechs such as Prosper, Lending Club, Kiva. These studies reveal an intriguing trend: loans funded within personal networks often exhibit higher repayment rates compared to conventional loans. Nevertheless, there remains a significant gap in research, particularly in understanding the quantification of social capital within social networks and its subsequent application in assessing an individuals creditworthiness. Trust and Reputation systems is an area of computer science which have attracted attention in various networking environments, including online social networks, wireless communication networks, multi-agent systems, and peer-to-peer networks. Trust and Reputation systems can use explicit or implicit information for decision making. The research, embarked on a bibliographic analysis of algorithms specifically designed for computing trust and reputation, which act as proxies for social capital in social networks. The algorithms selected for this study were eigentrust \\cite{conf/www/KamvarSG03}, tidaltrust \\cite and graph algorithms \\cite. They were applied in real cases of solidary credit arrangements observed in the community, more especifically a case of a ROSCA (Consorcio entre amigos) and two cases of peer-to-peer loans (Emprestimos entre amigos e familiares) that were derived from the former ROSCA. The objective was to evaluate the effectiveness and limitations of these algorithms and to address the challenges inherent in gathering trust and reputation data, considering its sensitive nature. Furthermore, the research extends to examining how these algorithms can predict the likelihood of creditworthiness using real dataset of indian villages, database provided in the paper The Diffusion of Microfinance By Abhijit Banerjee, Arun G. Chandrasekhar, Esther Duflo and Matthew Jackson. The goal of this research is to contribute to the development of more inclusive credit scoring methods, particularly targeting less-privileged segments of the population. By integrating interdisciplinary approaches, the study provides valuable insights into the potential of leveraging social capital and trust mechanisms to enhance financial inclusion and support economic empowerment in small communities.