The reputation model is designed to reduce risk for liquidity providers (LPs) by tracking the historical behavior of participants in the network.
It assigns implicit credibility to:
- Payers (clients)
- Freelancers (invoice originators)
The most important signal in the system is:
Does the payer settle invoices on time?
Each payer accumulates:
- Total invoices paid
- Total invoices defaulted
- Average payment delay
payer_score = paid_invoices / total_invoices
Enhancements:
- Time-weighted scoring
- Penalty for defaults
- Bonus for early payments
Freelancers are evaluated based on:
- % of invoices successfully funded
- % of invoices that defaulted
- Historical volume
This prevents:
- Fake invoices
- Low-quality counterparties
LPs use both scores:
risk = f(payer_score, freelancer_score, discount_rate)
Where:
- Higher discount_rate = higher perceived risk
- Lower payer_score = higher risk
- Invoice lifecycle (Pending → Funded → Paid / Defaulted)
- Payment history
- Default events
- Score computation
- Risk dashboards
- LP decision engines
- NFT-based reputation badges
- Credit delegation
- Dynamic discount pricing based on score
- ZK-based private credit scoring
Without reputation:
- LPs cannot price risk
- Capital becomes inefficient
- Defaults increase
With reputation:
- Better pricing
- More liquidity
- Scalable credit markets
The ILN reputation model transforms raw invoice data into:
Programmable creditworthiness
This is the foundation for decentralized invoice financing at scale.