followthemoney
A data model for anti corruption data modeling and analysis.
Decision gist · record as of 2026-08-14
Yes. followthemoney is actively maintained, has no known vulnerabilities, and solves a specific and well-defined problem for investigative and compliance workflows. The permissive MIT license poses no restrictions. Install friction is low and dependencies are mature. It is worth installing if you are building a data pipeline for financial crime, sanctions, or investigative journalism work.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.11 or later.
- The description notes that pyicu (a text-processing dependency) can be tricky to install on some systems; review its documentation if installation fails.
- Low friction install with a wheel distribution.
License · maintenance · safety
permissive license (permissive) — MIT License (permissive). You may use, modify, and distribute this package freely in commercial and private projects, provided you include the license notice and copyright attribution to Journalism Development Network, Inc. and OpenSanctions Datenbanken GmbH.
last release 2026-07-30 (15 days) · last repo commit 2026-08-01 · 86 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 81,103 downloads/mo, #14,249 on PyPI
Alternatives
Verify before relying
pip install followthemoney
from followthemoney import model
# Access the schema
schema = model.get('Person')
print(schema.label)- Whether pyicu is included in the standard install or requires separate setup steps on all platforms.
- Performance characteristics when processing large datasets or complex ownership networks.
- Compatibility with downstream tools that consume followthemoney-modeled data.
What it is and what it does
followthemoney is a data model and toolkit designed for investigators, journalists, and compliance researchers working with financial crime and anti-corruption data. Rather than aiming for an ideal theoretical model, it prioritizes practical usability by simplifying complex legal concepts into a working structure for efficient data processing. The package defines schemas for common entities (people, companies, assets, payments, ownership relations, court cases) and provides validation and normalization code to ensure data quality.
Beyond the schema itself, followthemoney includes a command-line tool for processing and transforming data, mapping from tabular formats into the model, and generating RDF/OWL representations. It integrates with established libraries like SQLAlchemy, Pydantic, RDFlib, and NetworkX to support both relational and graph-based workflows. The project maintains active documentation and an explorer for schema definitions, making it suitable for teams building investigative data pipelines.
Use it for
- Build a database of corporate ownership networks and beneficial ownership chains for anti-corruption investigations.
- Normalize and validate entity records (names, addresses, identifiers) from multiple sources before merging into a single dataset.
- Transform spreadsheet or CSV data into a standardized entity-relationship model for compliance reporting.
- Generate RDF/OWL exports of financial crime data for semantic web tools and linked-data applications.
- Map court case records, sanctions lists, and payment flows into a unified schema for cross-domain analysis.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
followthemoney is actively maintained, has no known vulnerabilities, and solves a specific and well-defined problem for investigative and compliance workflows. The permissive MIT license poses no restrictions. Install friction is low and dependencies are mature. It is worth installing if you are building a data pipeline for financial crime, sanctions, or investigative journalism work.
Install
followthemoney on PyPI
Before you install
Low friction install with a wheel distribution. Active maintenance with recent releases; 17 runtime dependencies are substantial but well-established libraries (pydantic, sqlalchemy, rdflib, networkx). Requires Python 3.11+.
Requires Python 3.11 or later. The description notes that pyicu (a text-processing dependency) can be tricky to install on some systems; review its documentation if installation fails.
License in practice
MIT License (permissive). You may use, modify, and distribute this package freely in commercial and private projects, provided you include the license notice and copyright attribution to Journalism Development Network, Inc. and OpenSanctions Datenbanken GmbH.
Quickstart
pip install followthemoney
from followthemoney import model
# Access the schema
schema = model.get('Person')
print(schema.label)Verify before relying
- Whether pyicu is included in the standard install or requires separate setup steps on all platforms.
- Performance characteristics when processing large datasets or complex ownership networks.
- Compatibility with downstream tools that consume followthemoney-modeled data.
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.11 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 17 packagesbabelbanalclicknetworkxnormalityopenpyxlorjsonphonenumbersprefixdatepydanticpytzpyyamlrdflibrequestsrichrigoursqlalchemy |
| Maintenance | Actively maintained 15 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 81,103 / month, #14,249 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Intended Audience :: DevelopersLicense :: OSI Approved :: MIT LicenseOperating System :: OS IndependentProgramming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13 |
Evidence: followthemoney-4.10.1-py3-none-any.whl
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