osint-investigation

Automating public records investigation is done by integrating with sources like SEC EDGAR and OpenCorporates to perform entity resolution and evidence chaining. This framework handles data acquisition to trace financial relationships.

Evidence chaining in OSINT due diligence constructs structured evidence chains with explicit confidence levels. It connects disparate public records data points to validate findings during entity resolution and timing correlation.

You can use Python with pandas and BeautifulSoup for sanctions screening by querying data sources like OFAC SDN. The framework processes this data to perform entity resolution and identify suspicious patterns.

This OSINT framework works with corporate records and SEC EDGAR filings by automating data acquisition and cross-link analysis. It resolves entities across diverse sources with confidence levels for due diligence.

Tracing financial relationships using public records data involves integrating sources like ICIJ Offshore Leaks and USAspending. The framework performs timing correlation and statistical analysis to identify suspicious patterns.

Limitations of using Python stdlib for entity resolution include requiring specific scripts for each data source. While it handles cross-link analysis, complex timing correlation across diverse sources demands explicit confidence levels.