FailSafe AI

AI-Based Startup Failure Prediction & Risk Sharing Platform

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FailSafe AI

Inspiration

This project is inspired by real-life experiences.

Growing up, I observed how traditional chit fund systems in villages functioned as informal financial safety nets. Individuals contributed regularly, and during times of crisis, the pooled funds helped members recover financially.

At the same time, I witnessed a startup failure within my family. My cousin’s startup struggled financially and eventually failed. During that difficult period, my grandmother used funds from a chit system to repay an education loan.

This contrast led to a central question:

Can a modern, AI-driven system replicate this risk-sharing model for startups?

FailSafe AI was developed as an answer to this idea.

What it does

FailSafe AI is an intelligent platform that:

  • Predicts the risk of startup failure.
  • Computes a risk score based on financial and operational metrics.
  • Classifies startups into risk categories: High, Moderate, and Low.
  • Determines premium and payout values based on risk levels.
  • Provides data-driven insights for decision-making.
  • Verifies whether a startup failure claim is genuine or potentially fraudulent.

How we built it

1. Data Layer

The system uses a SQLite database to store structured startup data, including:

  • Monthly revenue and expenses
  • Growth rate
  • Runway (in months)
  • Team size
  • Market risk
  • Funding

2. Risk Model

A custom risk scoring function was designed to quantify startup health:

$$\text{Risk Score} = \text{Burn Rate} + \text{Growth Decline} + \text{Runway Risk} + \text{Team Risk} + \text{Market Risk}$$

Where: $$\text{Burn Rate} = \max(0, \text{Expenses} - \text{Revenue})$$

Additional penalties are applied based on:

  • Negative growth
  • Runway ( \le 6 ) months
  • Small team size
  • High market risk

3. Machine Learning

  • Model used: Random Forest Classifier
  • Techniques applied:
    • Label Encoding for categorical variables
    • One-hot encoding for industry classification

The model predicts the startup’s risk category based on input features.

4. Frontend

The user interface is built using Streamlit and provides:

  1. A dashboard for portfolio-level analysis
  2. A risk calculator for individual startups
  3. A failure verification module

5. Financial Model

The platform incorporates a risk-sharing mechanism inspired by pooled finance systems:

$$\text{Premium} = \text{Funding} \times \text{Risk Rate}$$ $$\text{Payout} = \text{Funding} \times \text{Payout Rate}$$

Challenges we ran into

  • Database integration: Aligning schemas between the UI and the backend.
  • Data Quality: Handling missing or inconsistent data in the training set.
  • Algorithm Design: Designing a balanced and realistic risk scoring formula.
  • UI/UX: Building a clean, responsive interface using Streamlit.

Accomplishments that we're proud of

  • Developed a complete end-to-end AI-based application.
  • Successfully integrated database, machine learning, and frontend components.
  • Designed a practical risk-sharing model inspired by real-world systems.
  • Built an interactive and functional dashboard.

What we learned

  • Machine learning model development and evaluation.
  • Data preprocessing and feature engineering.
  • Database management using SQLite.
  • Full-stack development using Streamlit.
  • Financial modeling for risk assessment.

What's next for FailSafe AI

  • Integration of real-time startup datasets.
  • Enhancement using advanced machine learning models.
  • Deployment as a scalable web application.
  • Improved fraud detection using text analysis.
  • Expansion into a comprehensive startup risk management system.

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