actuarialmodel

v0.0.1 suspicious
6.0
Medium Risk

Actuarial Model Framework

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits low risks in terms of network calls, shell execution, and obfuscation. However, the metadata risk score is high due to the repository's suspicious characteristics.

  • High metadata risk due to recent creation, low activity, and single contributor
  • Maintainer's history raises concerns
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires external services.
  • Shell: No shell execution patterns detected, indicating no immediate risk of command execution.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The repository is suspicious due to its recent creation, low activity, and single contributor. The maintainer's history also raises concerns.

🔬 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: proton.me>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 10.0

Git history flags: Repository created very recently: 5 day(s) ago (2026-05-31T15:47:53Z)

  • Repository created very recently: 5 day(s) ago (2026-05-31T15:47:53Z)
  • Repository has zero stars and zero forks
  • Very few commits: 2 total
  • Single contributor with only 2 commit(s) — possibly throwaway account
⚠ Maintainer History score 6.0

3 maintainer concern(s) found

  • Only one version has ever been released — brand new package
  • 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 actuarialmodel 
Develop a fully-functional mini-app that leverages the 'actuarialmodel' Python package to perform actuarial analysis on insurance claims data. This app will allow users to upload their own datasets, select different actuarial models for analysis, and visualize the results. Here’s a step-by-step guide on how to build it:

1. **Setup**: Install necessary Python packages including 'actuarialmodel', 'pandas', 'matplotlib', and 'streamlit'.
2. **Data Input**: Create a user-friendly interface where users can upload CSV files containing insurance claim data.
3. **Model Selection**: Implement a feature that allows users to choose from various actuarial models available in the 'actuarialmodel' package, such as the Chain Ladder method or Bornhuetter-Ferguson technique.
4. **Analysis Execution**: Upon selection of a model, execute the chosen actuarial analysis using the selected dataset and model from the 'actuarialmodel' package.
5. **Visualization**: Display the results of the analysis through interactive plots and charts using matplotlib or similar libraries.
6. **Report Generation**: Allow users to generate and download a PDF report summarizing the key findings from the analysis.
7. **Documentation & Testing**: Ensure thorough documentation and testing of all functionalities.

Suggested Features:
- Real-time feedback during file upload.
- Option to clean and preprocess data before analysis.
- Multiple model comparison feature.
- Integration with cloud storage services for saving and sharing reports.

The 'actuarialmodel' package is utilized throughout the project for its robust actuarial modeling capabilities, providing accurate and efficient analysis of insurance claims data.