Summary
Build an automated matching engine that suggests invoice-lender pairs based on risk profile, currency preference, yield requirements, and historical performance.
Problem
Lenders manually browse the marketplace to find suitable invoices. This is time-consuming and often results in missed opportunities or poor fits.
Requirements
- Lender risk profile (conservative, moderate, aggressive)
- Currency preference (XLM, USDC, both)
- Yield requirement (minimum APY)
- Invoice matching algorithm: score = f(risk, currency, yield, history)
- Suggested matches displayed in marketplace
- Match quality indicator (excellent, good, fair)
- Override: lender can still browse all invoices
Technical Details
- Scoring algorithm: weighted sum of match factors
- Risk assessment: invoice age, originator history, amount
- Currency match: exact match vs. conversion cost
- Yield comparison: required vs. available
- ML potential: train on historical offer-acceptance patterns
Acceptance Criteria
Related Issues
Summary
Build an automated matching engine that suggests invoice-lender pairs based on risk profile, currency preference, yield requirements, and historical performance.
Problem
Lenders manually browse the marketplace to find suitable invoices. This is time-consuming and often results in missed opportunities or poor fits.
Requirements
Technical Details
Acceptance Criteria
Related Issues