AI Loan Obligation & Covenant Tracker

An AI-powered tool that automatically extracts, tracks, and monitors loan obligations and covenants from agreements, helping lenders and borrowers prevent missed deadlines and reduce compliance risk.

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Inspiration

Loan agreements are packed with critical obligations such as financial covenants, reporting deadlines, and notification requirements. In practice, these are often tracked manually using spreadsheets, calendar reminders, and emails. During research, it became clear that missed obligations are rarely intentional they usually happen due to oversight, complexity, or lack of centralized tracking. The inspiration for this project came from the need for a simple, explainable, and automated way to extract and monitor loan obligations without relying on manual processes. The goal was to create a tool that could realistically be used by banks, lenders, and borrowers to reduce compliance risk and improve operational efficiency.

What it does

The AI Loan Obligation & Covenant Tracker automatically processes loan agreements to extract borrower obligations and track their compliance status. Users can upload a loan agreement PDF, paste contract text, or use a sample agreement. The system identifies obligations such as financial covenants, reporting requirements, insurance clauses, and notification duties. Each obligation is categorized, assigned a risk level, and monitored for upcoming or missed deadlines. The results are displayed in an interactive dashboard that highlights high-risk obligations and upcoming compliance events.

How we built it

The application is built using Python and Streamlit for a simple and interactive user experience. PDF documents are processed using text extraction techniques, while rule-based natural language processing is used to identify obligations and deadlines from legal text. Each extracted obligation is evaluated using a risk scoring mechanism based on its type, frequency, and potential consequences of non-compliance. The system then assigns a compliance status—Compliant, Due Soon, or Missed—based on current dates. All processing is done locally to ensure data privacy and transparency. Synthetic loan agreement documents are used for demonstration to avoid handling real or sensitive financial data.

Challenges we ran into

One of the main challenges was handling the wide variation in legal language used in loan agreements. Obligations can be expressed in many different formats, making accurate extraction difficult. Designing rules that were flexible enough to capture meaningful obligations without overcomplicating the system required careful iteration. Another challenge was balancing accuracy with explainability. Instead of using opaque models, the system was intentionally designed to be deterministic and interpretable, which is important for regulated financial environments.

Accomplishments that we're proud of

Successfully built an end-to-end working application as a solo project Automated extraction and tracking of complex loan obligations Designed a clean, intuitive dashboard understandable by non-technical users Implemented risk categorization and deadline monitoring Maintained full data privacy with local-only processing

What we learned

This project highlighted how even simple, well-designed automation can significantly reduce operational risk in financial processes. It reinforced the importance of explainability and trust when building tools for regulated domains like finance. Additionally, it provided hands-on experience in processing legal text and translating unstructured documents into actionable insights.

What's next for AI Loan Obligation & Covenant Tracker

Future improvements include support for multi-document loan facilities, configurable risk thresholds, email or calendar alerts for upcoming deadlines, and integration with loan management systems. With further enhancements, the platform could scale to monitor large loan portfolios across financial institutions.

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