CovenantGuard AI

A real-time AI-powered dashboard that detects critical loan covenant breaches early and explains risks clearly, helping analysts move from reactive reporting to proactive decision-making.

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Inspiration

What it does

How we built it

Challenges we ran into

Inspiration

While talking to finance and credit professionals, I noticed that loan covenant monitoring is still heavily dependent on spreadsheets and manual checks. This reactive approach often detects problems too late, after financial damage has already occurred. I wanted to build a system that shifts monitoring from delayed reporting to real-time, proactive risk detection.

That idea became CovenantGuard AI.

What it Does

CovenantGuard AI is a real-time monitoring dashboard that evaluates loan covenants continuously and highlights the most critical risks first.

Key capabilities include:

  • Real-time covenant breach detection
  • Risk classification (Safe, Watch, Critical)
  • Clear, plain-English explanations for each breach
  • Simulation mode to test "what-if" scenarios
  • A lightweight analyst assistant for quick queries

Instead of reviewing long documents and spreadsheets, analysts can instantly see where attention is needed.

How We Built It

The system was designed with simplicity, speed, and explainability in mind.

  • Backend: FastAPI (Python) for high-performance async APIs
  • Validation: Pydantic to ensure strict data integrity
  • Logic Engine: A deterministic, rule-based engine for accurate risk classification
  • Frontend: Vanilla JavaScript, HTML, and CSS for a fast, dependency-free dashboard
  • Deployment: Backend on Render, frontend on Vercel

We intentionally avoided black-box models for risk calculations to ensure accuracy and auditability.

Challenges We Faced

  • Translating complex financial covenants into deterministic, machine-readable rules
  • Prioritizing clarity and explainability over AI hype
  • Shipping a complete, working prototype within hackathon time limits
  • Designing a clean UI that highlights risk without overwhelming users

These challenges pushed us to focus on strong system design and clear trade-offs.

What We Learned

  • Well-architected logic can solve real problems without unnecessary complexity
  • Explainability is critical in financial systems
  • A focused, minimal tech stack can deliver powerful results
  • Solving the business problem matters more than adding flashy features

What's Next

  • Automated PDF covenant extraction using LLMs
  • Real-time notifications via email or Slack
  • Immutable audit logs for compliance teams
  • User authentication and role-based access

CovenantGuard AI is designed to be practical, auditable, and production-ready—proving that smart architecture can outperform heavy enterprise tools.

Accomplishments that we're proud of

What we learned

What's next for CovenantGuard AI

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