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Data validation and cleansing  \n2. Feature aggregation and enrichment  \n3. Key Risk Indicator (KRI) computation  \n4. Anomaly estimation  \n5. Composite enterprise risk scoring  \n6. Automated narrative risk reporting  \n\n**Output**\n- 🔢 Overall Risk Score (0–100)  \n- 🚦 Risk Classification (Low / Medium / High)  \n- 📊 Key Risk Indicators (KRIs)  \n- 🧠 Explainable drivers of risk  \n- 📝 Plain-English executive summary  \n- ⬇️ Downloadable scored dataset  \n\n---\n\n## Live Demo Walkthrough\n\n1. Open the live demo link  \n2. Upload a CSV file  \n3. View:\n   - computed KRIs  \n   - enterprise risk score  \n   - explainable narrative summary  \n4. Download the scored output  \n\nNo login. No setup.\n\n---\n\n## Why This Matters\n\nMost risk tools either:\n- present dashboards without explanation, or  \n- generate black-box scores that cannot be defended to auditors or leadership.\n\nThis engine prioritises:\n- **Explainability**\n- **Governance-aligned metrics**\n- **Audit-friendly outputs**\n- **Executive-ready narratives**\n\nIt mirrors how AI is actually adopted inside regulated enterprise environments.\n\n---\n\n## Use Cases\n\n- Enterprise Risk Management (ERM)  \n- Internal Audit \u0026 Assurance  \n- Operational Resilience  \n- Compliance \u0026 Controls  \n- Cyber / IT Risk Analytics  \n\n---\n\n## Repository Structure\n\n.\n├── data/               # Sample and reference datasets\n├── diagrams/           # Architecture and system diagrams\n├── docs/               # Design documentation\n├── examples/           # Example inputs\n├── outputs/            # Generated outputs and reports\n├── src/                # Core risk logic and analytics\n├── streamlit_app.py    # Live demo application\n├── requirements.txt    # Dependencies\n├── TECH_NATION_EVIDENCE.md\n└── README.md\n\n---\n\n## Tech Stack\n\n- Python  \n- Pandas / NumPy  \n- Streamlit (live UI)  \n- Statistical \u0026 rule-based risk modelling  \n- Explainable narrative generation  \n- Render (production deployment)  \n\n---\n\n## Design Philosophy\n\n- Explainability over black-box accuracy  \n- Enterprise-aligned risk metrics  \n- Human-readable reporting  \n- Production realism (live deployment, real inputs)  \n\n---\n\n## Limitations (Intentional)\n\n- Heuristic and statistical methods (no deep learning yet)  \n- Demo-scale datasets  \n- Single-tenant deployment  \n\nThese choices prioritise clarity, governance, and interpretability.\n\n---\n\n## Planned Enhancements\n\n- Machine learning–based anomaly detection  \n- Time-series risk trend analysis  \n- PDF executive risk reports  \n- API-first FastAPI version  \n- Expanded CI/CD test coverage  \n\n---\n\n## Project Ownership \u0026 Contributors\n\nThis project was led and architected by **Ibrahim Akinyera**, who designed the AI architecture, scoring logic, anomaly detection approach, and automated narrative reporting pipeline as part of applied enterprise risk research and UK Global Talent technical evidence.\n\n**Key Contributors:**\n- **Ibrahim Akinyera** — AI/ML Lead \u0026 Project Architect  \n- **Busayo Odukoya** — Technology Risk Expert (domain input, risk frameworks, governance alignment)  \n\nAll final technical design decisions, system architecture, and production deployment were led by Ibrahim Akinyera.  \n\nThis project forms part of my applied work in:\n- AI-driven decision intelligence  \n- Enterprise risk analytics  \n- Production-ready AI systems  \n\n---\n\n## License\n\nMIT License","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fakinyeraakintunde%2Fenterprise-risk-intelligence-engine","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fakinyeraakintunde%2Fenterprise-risk-intelligence-engine","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fakinyeraakintunde%2Fenterprise-risk-intelligence-engine/lists"}