Equitable Capital Optimization AI

A reproducible research prototype for predictive capital-readiness analysis, model explainability, fairness auditing, and equity-aware capital-allocation simulation for underserved U.S. entrepreneurial ecosystems.

The project is inspired by:

An AI-Powered Framework for Equitable Capital Optimization: Leveraging Predictive Intelligence to Empower Underserved Entrepreneurial Ecosystems in the U.S.

Related publication: ResearchGate

Why this project exists

Access to capital is not only a prediction problem. It is also an allocation, transparency, and measurement problem. This repository separates those concerns into four independently testable components:

Responsible-use boundary

Research and educational use only.

This project must not be used to make real lending, credit, investment, employment, housing, insurance, benefits, or eligibility decisions.

The predictive model intentionally excludes protected personal characteristics. Structural context indicators are used for research auditing and allocation simulation, not as protected-trait proxies for real-world underwriting.

Architecture

flowchart LR
    A[Synthetic Business Data] --> B[Feature Pipeline]
    B --> C[Random Forest Model]
    C --> D[Capital Readiness Score]
    C --> E[Local Explanation]
    D --> F[Fairness Audit]
    D --> G[Allocation Simulator]
    F --> H[Research Dashboard]
    G --> H
    E --> H

See Architecture and Methodology.

Key capabilities

• Reproducible synthetic U.S. small-business/startup data generation
• Scikit-learn preprocessing and Random Forest classification pipeline
• Holdout evaluation with ROC-AUC, accuracy, precision, recall, F1, and Brier score
• Capital Readiness Score derived from predicted funding-success probability
• Global feature-importance reporting
• Applicant-level local sensitivity explanations
• Structural-context fairness audit and selection-rate comparison
• Efficiency-only and equity-aware capital-allocation simulation
• Interactive Streamlit research dashboard
• Automated tests and linting in GitHub Actions
• Model card, citation metadata, contribution guide, and security policy

Python package

The reusable research code lives in the equitable_capital Python package.

After a release is published to PyPI, install it with:

pip install equitable-capital-optimization-ai

Example:

from equitable_capital import (
    allocate_capital,
    fairness_audit,
    generate_synthetic_startups,
    train_model,
)

data = generate_synthetic_startups()
result = train_model(data)
audit = fairness_audit(result.scored_data)

For local development, install the repository in editable mode:

pip install -e ".[dev]"

Quick start

git clone https://github.com/sakera023/equitable-capital-optimization-ai.git
cd equitable-capital-optimization-ai
python -m venv .venv

Activate the environment.

Windows

.venv\Scripts\activate

macOS/Linux

source .venv/bin/activate

Install and run:

pip install -r requirements.txt
streamlit run app.py

Development checks:

pip install -r requirements-dev.txt
ruff check src tests app.py
python -m pytest -q

Repository structure

.
├── app.py
├── pyproject.toml
├── requirements.txt
├── requirements-dev.txt
├── Makefile
├── src/
│   └── equitable_capital/
│       ├── __init__.py
│       ├── allocation.py
│       ├── config.py
│       ├── data.py
│       ├── explainability.py
│       ├── fairness.py
│       └── modeling.py
├── tests/
├── docs/
├── .github/
├── CITATION.cff
├── CONTRIBUTING.md
├── SECURITY.md
├── CHANGELOG.md
└── LICENSE

Data design

The repository uses synthetic data by default. This avoids exposing private financial records, makes the project reproducible, and prevents the demonstration from implying real-world predictive validity.

Predictive features include revenue, growth, cash runway, employees, operating history, debt-service coverage, digital adoption, market demand, management capacity, requested capital, industry, and state.

Model evaluation

The application reports ROC-AUC, accuracy, precision, recall, F1 score, and Brier score. These metrics evaluate the synthetic demonstration only.

Fairness and equity analysis

A structural barrier index is built from contextual variables such as low-income area, rural area, limited finance access, and digital adoption.

The index is reserved for post-model fairness diagnostics and research simulation of equity-aware allocation policies. It is not included in the predictive training features.

Reproducibility

The synthetic data generator and model pipeline use explicit random seeds. Tests verify data ranges, prediction bounds, allocation-budget constraints, and fairness-audit outputs. CI runs on every push and pull request.

Research roadmap

Planned extensions include gradient-boosted model benchmarks, probability calibration, SHAP, temporal/geographic validation, county-level opportunity maps, Census/SBA/CDFI public-data integrations, constrained optimization, and uncertainty analysis.

See Research Roadmap.

Citation

If you use the software, cite the repository metadata in CITATION.cff. If you use the associated research concept, cite the publication separately and clearly distinguish research findings from this software prototype.

Contributing

See CONTRIBUTING.md.

Security

Do not publish secrets, private financial information, or real applicant records in issues or pull requests. See SECURITY.md.

License

MIT License. See LICENSE.

Maintainer

Sakera Begum