LoanChain: Visual Loan Trading Desktop

Inspiration

Complex loan agreements are trapped in 50-page PDFs that take days to understand during trading. Traders need instant visual clarity of cash flows, covenants, and collateral triggers. LoanChain was born from this pain point—transforming opaque documents into interactive flowcharts for one-click trade decisions.

What it does

LoanChain is a desktop application for Transparent Loan Trading that converts loan agreements into visual flowcharts and one-click trade simulation sheets.

Core Workflow

1. Upload a PDF/Word loan agreement
   
2. Auto-generates an interactive flowchart showing cash flows, covenants, and collateral triggers
   
3. Drag & drop buyer/seller to instantly simulate a trade
   
4. Export a visual deal sheet for quick sharing and approvals
   

Visual Flow Representation

$$ \text{Principal Flow} \rightarrow \text{Quarterly Interest} \rightarrow \text{Covenant Triggers} \rightarrow \text{Collateral Calls} $$

How we built it

Tech Stack

• Desktop Shell: Electron
  
• Frontend UI: React + TypeScript + TailwindCSS
  
• PDF Parsing: pdf.js + lightweight NLP extraction
  
• Flowchart Visualization: React Flow
  
• Export Engine: html-to-image + jsPDF
  
• State + Storage: Local storage / filesystem (offline-first)

Offline Mode

LoanChain runs completely offline:

• Documents are parsed locally
  
• No cloud dependency
  
• Trade sheets and diagrams are generated on-device for privacy and compliance
  

Challenges we ran into

1) Document parsing inconsistency

Loan agreements come in different structures and formatting styles.

Solution:

• Custom regex extraction for key fields
  
• Clause detection heuristics
  
• Fallback manual tagging for edge cases
  

2) Complex flow logic for syndicated loans

Syndicated loans involve multi-party repayment flows and nested conditional triggers.

Solution:
Recursive node generation mapping legal clauses to visual paths.

Example logic:

\text{If } \frac{\text{Debt}}{\text{EBITDA}} > 4.0 \text{ then Collateral Call}
3) Performance issues with large PDFs
Large PDFs caused slow rendering and early parser crashes.

Solution:

Web workers for background parsing

Progressive rendering

Optimized flowchart node generation

Accomplishments that we're proud of
Built a desktop-first fintech prototype with premium UI/UX

Converted complex loan documents into interactive flowcharts

Implemented drag-and-drop trade simulation with instant visuals

Generated exportable deal sheets to replace long email threads

Achieved a privacy-first workflow with 100% offline execution

What we learned
Financial documents require both automation + human validation

Visual abstraction makes legal terms faster to understand and verify

Performance tuning is essential for real-world PDFs

Offline-first architecture matters for compliance-sensitive institutions

What's next for LoanChain Desktop
Smarter clause extraction using fine-tuned NLP models

OCR support for scanned loan agreements

Advanced pricing and risk analytics (scenario testing, exposure graphs)

Audit logs + version history for compliance

Multi-role workflow support (trader, analyst, compliance reviewer)

Built With

• electron
• pdf.js
• react
• reactflow
• tailwindcss
• typescript