EDRAF TECH

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Research on predatory economies, loan shark practices, and digital lending in South Asia and beyond highlights a significant portion of the population remaining underbanked and excluded from affordable financial services. This exclusion is often due to the inability to demonstrate financial solvency, lack of formal credit histories, absence of collateral, or residence in 'no-go areas' avoided by financial institutions due to corruption and criminal activities. Consequently, individuals in these circumstances frequently fall prey to unscrupulous money lenders, fraudulent apps, and extortionist Fin-techs. In response to this challenge, we have developed an innovative solution premised on the idea that assessing financial risks requires consideration not only of financial data but also socio-cultural data. Our methodology and algorithm draw from extensive ethnographic knowledge, providing a foundation for the Ethnographic Driven Risk Analysis Framework (EDRAF). This framework aims to manage risks within the social lending sector and beyond. EDRAF includes a mobile app designed to collect and map debt and credit histories within households and across wider family networks. Additionally, it gathers anthropological data to evaluate the economic and social impact of loans. This approach facilitates the creation of fair credit ratings and enables the benchmarking of a loan's future social and economic implications. Furthermore, it identifies potential factors such as conflict, corruption, or involvement in criminal networks that may undermine the success of future social lending initiatives. By doing so, we aim to contribute promoting ethical financial practices. Our next step involves market validation in collaboration with UCLB Business and expanding he product into various markets across both the Global South and Global North.