Credit risk scoring engine — A Python library that calculates credit risk scores based on financial indicators using a weighted scoring model.
Disclaimer: For educational purposes only — not financial advice.
Inspired by AI credit risk analysis trends.
graph TD
A[Applicant Data] --> B[CreditScan Engine]
B --> C[Income Ratio Scorer]
B --> D[Payment History Scorer]
B --> E[Credit Utilization Scorer]
B --> F[Account Age Scorer]
C --> G[Weighted Composite Score]
D --> G
E --> G
F --> G
G --> H[Risk Classification]
H --> I[Credit Report]
I --> J{Decision}
J -->|Excellent / Good| K[Approved]
J -->|Fair| L[Review]
J -->|Poor / Very Poor| M[Declined]
pip install -e .from creditscan import CreditScan
engine = CreditScan()
applicant = {
"name": "Jane Doe",
"income": 85000,
"debt": 12000,
"payments_on_time": 45,
"payments_late": 3,
"payments_missed": 1,
"credit_used": 4500,
"credit_limit": 20000,
"account_age_years": 7,
}
assessment = engine.score(applicant)
print(f"Score: {assessment.score}")
print(f"Risk: {assessment.risk_class}")
report = engine.generate_report(assessment)
print(report)applicants = [applicant_a, applicant_b, applicant_c]
ranking = engine.compare_applicants(applicants)
for rank, result in enumerate(ranking, 1):
print(f"#{rank} {result.applicant_name} — {result.score} ({result.risk_class})")make install # Install in editable mode with dev deps
make test # Run test suite
make lint # Lint with ruff
make format # Format with blackCreditScan/
├── src/creditscan/
│ ├── __init__.py # Public API
│ ├── core.py # CreditScan engine
│ ├── config.py # Weights & thresholds
│ └── utils.py # Scoring helpers & report formatting
├── tests/
│ └── test_core.py # Unit tests
├── docs/
│ └── ARCHITECTURE.md # Design documentation
├── pyproject.toml
├── Makefile
└── README.md
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