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Forecasting Financial Resilience: An Analysis of Practices and Limitations in Predicting Trends - A Case Study of Microcredit in Tanzania

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dc.contributor.author Mwapashua, Fujo
dc.contributor.author Katwale, Samwel
dc.contributor.author Dida, Mussa A.
dc.date.accessioned 2024-08-20T12:12:21Z
dc.date.available 2024-08-20T12:12:21Z
dc.date.issued 2024
dc.identifier.citation Fujo, M.H , Katwale, S. & Mussa Ally Dida,M.A (2024).Forecasting Financial Resilience: An Analysis of Practices and Limitations in Predicting Trends - A Case Study of Microcredit in Tanzania. The Nelson Mandela AFrican Institution of Science and Technology. en_US
dc.identifier.isbn 978-81-970423-1-7
dc.identifier.isbn 978-81-970423-5-5
dc.identifier.uri http://repository.mocu.ac.tz/xmlui/handle/123456789/1323
dc.description A full text article from the community of Information and Communication Technology en_US
dc.description.abstract This research paper delves into the intricate landscape of financial resilience within Tanzanian microcredit institutions, focusing on predictive methodologies and the integration of Artificial Intelligence (AI) for enhanced forecasting accuracy. Through an exhaustive exploration of traditional practices and emerging AI-driven solutions, this study examines the evolving strategies and limitations encountered in predicting financial trends within this dynamic sector. Employing a mixed methods approach encompassing diverse case studies across key Tanzanian regions - Dar-es-Salaam, Arusha, and Kilimanjaro - the research garnered insights into localized complexities, historical evolution, and direct impact on bolstering financial resilience. Findings underscored the multifaceted objectives pursued by microcredit institutions in trend projection, emphasizing the primary goals of optimizing investment strategies, managing liquidity effectively, and planning for sustainable growth and expansion. While traditional methodologies demonstrated some efficacy, challenges in data quality, interpretation, and predictive analytics expertise emerged as impediments to accurate trend projection. Proposed AI based solutions offered promising outcomes, with anticipated benefits including improved prediction accuracy, enhanced decision-making, and potential cost savings. However, concerns regarding data security, expertise, and implementation costs pose notable challenges to widespread AI integration. Therefore, the research advocates for the integration of AI technologies to fortify predictive capacities within Tanzanian microcredit institutions. It emphasizes the imperative nature of investing in resources and expertise to leverage AI potential for sustainable growth and heightened forecasting accuracy in this rapidly evolving financial landscape. This study contributes essential insights into the challenges, opportunities, and potential pathways for leveraging advanced technologies in enhancing financial resilience within microcredit institutions, fostering a more sustainable and prosperous future for Tanzania microcredit sector. en_US
dc.language.iso en en_US
dc.publisher The Nelson Mandela AFrican Institution of Science and Technology en_US
dc.relation.ispartofseries Vol. 9;
dc.subject Financial en_US
dc.subject Resilience en_US
dc.subject SACCOS en_US
dc.subject Microcredit sector en_US
dc.subject Economic growth en_US
dc.title Forecasting Financial Resilience: An Analysis of Practices and Limitations in Predicting Trends - A Case Study of Microcredit in Tanzania en_US
dc.type Article en_US


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