AI for Better Financial Allocation in the Third Sector
DOI:
https://doi.org/10.21070/jicte.v10i1.1717Keywords:
Artificial Intelligence, Financial Resources, Third Sector, Predictive Analytics, Digital GovernanceAbstract
General Background Effectively managing financial resources within civil society organizations is critical to ensuring long-term operational sustainability and maximizing social value. Specific Background Over the past thirty years, the third sector has shifted from a marginal economic player into a vital partner for delivering social services, yet organizations continue to rely on unstable funding sources and conventional financial planning models. Knowledge Gap While current literature addresses e-government digitalization, there is a clear lack of empirical analysis regarding the structural readiness, technical barriers, and specific performance metrics governing the integration of predictive analytics within nonprofit budgeting workflows. Aims This study analyzes the core challenges facing third-sector resource distribution and evaluates how artificial intelligence can optimize allocation efficiency, transparency, and donor trust. Results Utilizing global case studies from The Kids Cancer Project and Mercy Corps, the investigation demonstrates that integrating analytical and generative tools can improve financial efficiency by up to 80% over traditional systems, shorten reporting timelines from days to hours, and ensure data-driven distributive equity. Novelty This paper provides a newly synthesized SWOT matrix and an operational framework tailored specifically for resource allocation, linking donor patterns directly to real-time field needs. Implications The findings prove that adopting advanced data systems is no longer an optional future upgrade but a contemporary necessity to ensure accountability, reduce administrative waste, and maintain humanitarian service delivery across vulnerable communities.
Keywords: Artificial Intelligence, Financial Resources, Third Sector, Predictive Analytics, Digital Governance
Key Findings Highlights
Integrating intelligent decision systems can optimize third-sector financial resource allocation efficiency by up to 80% compared to legacy processes.
Case evaluations show that predictive models significantly cut reporting timelines from days to hours while increasing donor trust through real-time transparency.
Systemic organizational weaknesses, including limited technical funding and traditional management mindsets, present the primary hurdles to widespread digital adoption.
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