aml-analytics

v1.0.1 suspicious
4.0
Medium Risk

Open-source Python toolkit for AML detection and financial crime analytics — transaction graph analysis, anomaly scoring, SAR pattern matching, and SQL helpers for BSA/FinCEN compliance.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low risk for common malicious activities such as network calls, shell execution, obfuscation, and credential harvesting. However, the recent creation of the repository and the maintainer's limited history with PyPI raise some concerns about its authenticity and long-term support.

  • Low risk for common malicious activities
  • Repository and maintainer have limited history
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires external services.
  • Shell: No shell executions detected, reducing likelihood of executing arbitrary commands.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
  • Credentials: No credential harvesting patterns detected, indicating low risk of malicious activity.
  • Metadata: The repository was created recently and the maintainer has limited history with PyPI, indicating potential risk.

📦 Package Quality Overall: Medium (5.4/10)

✦ High Test Suite 9.0

Test suite present — 6 test file(s) found

  • Test runner config found: pyproject.toml
  • 6 test file(s) detected (e.g. test_anomaly.py)
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://github.com/Bhavesh0205/aml-analytics
  • Detailed PyPI description (4890 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
○ Low Type Annotations 1.0

No type annotations detected

  • No type annotations, py.typed marker, or stub files detected
◈ Medium Multiple Contributors 6.0

Limited contributor diversity

  • 2 unique contributor(s) across 22 commits in Bhavesh0205/aml-analytics
  • Two distinct contributors found

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: gmail.com>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 2.5

Git history flags: Repository created very recently: 6 day(s) ago (2026-05-31T15:59:50Z)

  • Repository created very recently: 6 day(s) ago (2026-05-31T15:59:50Z)
⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" appears to have only 1 package on PyPI (new or inactive account)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with aml-analytics 
Create a mini-application that leverages the 'aml-analytics' package to detect potential money laundering activities within a dataset of financial transactions. Your application should perform the following tasks:

1. **Data Importation**: Allow users to import a CSV file containing financial transaction data. This data should include fields such as transaction ID, account number, amount, date, and possibly other relevant details.

2. **Transaction Graph Analysis**: Utilize the 'aml-analytics' package to analyze the transaction network and identify unusual patterns or clusters of transactions that could indicate suspicious activity. Visualize these networks using graphs.

3. **Anomaly Scoring**: Implement a feature that scores each transaction based on predefined criteria for suspicious behavior. Use the 'aml-analytics' package to calculate anomaly scores and flag transactions that exceed a certain threshold.

4. **SAR Pattern Matching**: Integrate SAR (Suspicious Activity Report) pattern recognition into your application. The system should be able to match known SAR patterns against the imported data to highlight potential money laundering schemes.

5. **BSA/FinCEN Compliance**: Provide SQL helper functions to ensure that the analyzed data complies with BSA/FinCEN regulations. Users should be able to generate SQL queries that adhere to these compliance standards.

6. **User Interface**: Develop a simple web-based interface using Flask or Django where users can upload their data, view transaction graphs, see anomaly scores, and review flagged transactions.

7. **Reporting**: Enable users to generate detailed reports on suspicious activities detected, including visualizations and summaries of findings. These reports should be exportable in PDF or Excel formats.

The application should be designed to be user-friendly and efficient, providing valuable insights into potential financial crimes while adhering to regulatory standards.