SBIR Phase I: Automated Semantic Tagging for Tracking High-Impact Financial Flows

The broader commercial impact of this Small Business Innovation Research (SBIR) Phase I project lies in optimizing high-impact public and private sector investments through precise financial flow tracking and outcome measurement. While billions of dollars are distributed annually across corporate, philanthropic, and nonprofit channels, existing tracking solutions often suffer from systemic fragmentation and a lack of predictive capabilities. This project addresses these gaps by delivering an integrated software platform that streamlines reporting, mitigates regulatory ambiguity, and strengthens financial oversight. By leveraging advanced automation and analytics, the system empowers users to categorize expenditures, trace financial flows to specific localities, and forecast future risks with greater accuracy. Beyond supporting transparency and compliance, these enhancements foster accountability and facilitate data-driven decision-making across range of industries. Furthermore, the platform democratizes access to sophisticated analytics, providing smaller organizations with advanced capabilities previously reserved for those with significant information technology infrastructure. This project develops a cloud-native platform to automate the classification, benchmarking, and visualization of organizational financial data. The core innovation is a hybrid architecture that integrates semantic tagging, machine learning classifiers, and structured data extraction to harmonize disparate disclosures from regulatory bodies within the financial system. By applying automated forensic accounting principles, the platform aligns financial flows with performance indicators across federal, state, and municipal frameworks. The primary technical challenge involves fusing high-dimensionality unstructured text with structured reports and applying automated decision-support logic to generate actionable insights. This AI-driven architecture mitigates manual review latency, enforces reporting consistency, and enables non-technical stakeholders to interrogate complex datasets through intuitive interfaces. The system supports granular geospatial and temporal tracking, providing fund-level visibility down to the specific city or zip-code level. The project will engineer a production-grade prototype, optimize the Extract-Transform-Load (ETL) pipeline, and expand automated anomaly detection. Key technical milestones include validating the tagging and benchmarking modules via pilot testing with enterprise and nonprofit cohorts. The anticipated outcome is a scalable framework that enhances regulatory transparency, improves longitudinal outcome measurement, and accelerates the strategic deployment of high-impact capital. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.