Economic Crime Monitoring Lab

Synthetic transaction monitoring pipeline with alerting, management information (MI), and a dashboard. All data in this repository is synthetic, and the project is for defensive analytics only.

What it does

The pipeline generates synthetic customer, account, and transaction data; runs data quality checks; engineers monitoring features; applies rules and a baseline model; creates an alert queue and case records; produces weekly MI outputs; and serves a Streamlit dashboard for review.

Quickstart (macOS)

make install
make run
make dashboard

make dashboard starts the app at http://localhost:8501. Generated outputs are written locally and are not committed to the repository.

Outputs

• Default SQLite database: data/econ_crime_lab.db
• Reports directory: reports/YYYY-MM-DD/
• Key tables:
  • customers
  • accounts
  • transactions
  • alerts
  • cases
  • mi_weekly
  • model_metrics (if present)

How this maps to Economic Crime (Data)

• Controls and monitoring: rule-based controls, alert volume/rate monitoring, and false-positive review.
• MI: weekly reporting outputs and trend tracking for alerts and case outcomes.
• Governance: reproducible runs, logged assumptions and run metadata, and data quality checks.
• Threat awareness: simulated typologies (for example structuring-style behavior and unusual movement patterns) support pattern spotting and triage practice.

Repository layout

scripts/    # pipeline and utility entry points
src/        # econ_crime_monitoring_lab package
tests/      # automated tests
data/       # local synthetic data outputs
reports/    # dated MI outputs

Notes and limitations

• Synthetic data only.
• This is not a fraud playbook.
• The baseline model is illustrative.
• This is a learning project and not production monitoring.

Screenshots

Add screenshots by saving images into docs/ with those names.