osint-investigation

Cross-referencing public records for due diligence involves acquiring data from fragmented government and corporate databases and applying entity resolution to build evidence chains. This skill automates that acquisition and normalization process across heterogeneous data sources.

Yes, sanctions screening can be performed by fetching corporate filings from SEC EDGAR and cross-referencing them with federal contract data from USAspending. This process identifies potential conflicts of interest and maps financial relationships.

Resolving entities across disparate corporate databases requires normalized naming and fuzzy matching logic to link records. The skill applies these techniques to standardized CSV outputs to automatically connect related entities across different datasets.

Yes, you need Python standard library execution to perform statistical timing analysis on normalized CSV outputs. This environment is required to run the scripts that identify suspicious patterns, such as lobbying filings occurring near government contract awards.

Timing correlation for identifying suspicious financial patterns works by applying statistical timing analysis to normalized CSV outputs. It detects anomalies such as lobbying filings occurring in close temporal proximity to government contract awards across cross-referenced datasets.

Automated entity resolution in OpenCorporates and CourtListener data is limited by the accuracy of normalized naming and fuzzy matching logic. Heterogeneous data sources with inconsistent formatting can hinder the construction of reliable evidence chains.