FailSafe AI is an AI-driven platform designed to analyze startup financial health, predict failure risk, and simulate a structured risk-sharing mechanism similar to an insurance model.
The platform evaluates startups using key financial and operational indicators to generate a comprehensive risk profile. It combines rule-based scoring with machine learning to support data-driven decision-making for founders, investors, and risk analysts.
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Risk Intelligence Dashboard
Provides a real-time overview of startup risk metrics, including risk scores, expected failures, payouts, and system buffer. -
AI Risk Mentor
Identifies critical risk factors and offers actionable mitigation strategies based on financial inputs. -
Failure Verification System
Assesses whether a startup failure claim is genuine or potentially fraudulent using heuristic and ML-based validation. -
Premium and Payout Engine
Simulates a financial protection model by calculating premium contributions and estimated payouts. -
Machine Learning Integration
Uses a Random Forest classifier to predict risk levels and probability distributions.
- Frontend: Streamlit
- Backend: Python
- Database: SQLite
- Machine Learning: Scikit-learn (Random Forest Classifier)
- Data Processing: Pandas, NumPy
The system evaluates startups based on:
- Monthly Revenue and Expenses (Burn Rate)
- Growth Rate
- Runway (in months)
- Team Size
- Market Risk
- Funding Level
These inputs are used to:
- Compute a composite risk score
- Classify startups into risk categories (Low, Moderate, High)
- Estimate premium contributions and payout coverage
- Predict risk probabilities using a trained ML model
Streamlit App: http : https://failsafe-ai.streamlit.app/