III. The DreamPass Architecture
Core Strategy: Data Before Capital
Traditional approach:
1. Raise money for lending
2. Spend heavily on customer acquisition
3. Hope you attract creditworthy borrowers
DreamPass approach:
1. Give users valuable AI tool (free)
2. Build comprehensive financial dataset (consensual)
3. Launch lending when you have meaningful data
Phase 1: The AI Copilot (NOW - Recently Launched)
Free financial assistant that:
- Connects to bank accounts (via Plaid)
- Analyzes spending patterns
- Finds subscription waste
- Provides bill negotiation scripts
- Offers personalized financial guidance
Why users care: Many save $50-150/month on average (subscription cuts, bill negotiations)
Why this matters strategically:
- Zero CAC: Users join for value, not because we marketed to them
- Natural selection: Financially engaged users are better credit risks
- Data advantage: Build comprehensive financial behavior dataset
- Trust foundation: Prove value before asking for trust with loans
Reality check on user economics:
- Not everyone saves $150/month
- Results vary by user's financial situation
- Some users save nothing (already optimized)
- But enough save enough to create viral growth
Phase 2: Reputation Layer (Q4 2025 - Q1 2026)
Zero-knowledge proof system:
User generates cryptographic badges locally:
├─ "3-month emergency fund" (verifiable)
├─ "12-month payment streak" (verifiable)
├─ "Reduced debt 30% in 6 months" (verifiable)
└─ "Top quartile savings rate vs peers" (verifiable)
Badges are:
├─ Cryptographically verifiable
├─ User-controlled (can revoke)
├─ Time-stamped (expire if not maintained)
└─ Portable (work across protocols)
DreamPass Credit Score:
- Considers 100+ signals (vs FICO's 5 categories)
- Updates in real-time (vs FICO's monthly)
- Fully transparent (show exactly what matters)
- Users can see how to improve
Reality check:
- More signals ≠ automatically better predictions
- Need to validate against actual default data
- Will start conservatively, improve over time
- Regulatory approval required for actual lending use
Phase 3: Lending Protocol (Q2-Q3 2026)
Smart contract-based lending:
Loan Application
↓
AI + Traditional Underwriting (hybrid approach initially)
├─ DreamPass Score (behavior data)
├─ Traditional credit check (FICO/bureau data)
├─ Income verification
└─ Risk pool assignment
↓
Rate Calculation (transparent formula)
↓
User Accepts Terms
↓
Smart Contract Deployment
├─ Terms encoded on-chain
├─ Payment automation
└─ Transparent to community
↓
Funds Transfer (stablecoin → fiat via Circle/Bridge)
Pricing structure (transparent):
Your APR = Base + Risk + Insurance + Operations
Base Rate: 5-7% (cost of capital, varies with markets)
Risk Premium: 4-10% (based on credit assessment)
Insurance Pool: 1-2% (default protection)
Operations: 0.5-1% (actual costs, no profit margin)
─────
Target Range: 12-20% (vs typical 20-30% for similar credit)
Note: These are target ranges. Actual rates will be
determined by market conditions, regulatory requirements,
and actual default experience.
Three risk pools:
Conservative Pool (DreamPass Score: 700+)
- Target default rate: 1-2%
- Insurance premium: 1.0%
- Typical APR range: 12-15%
- Features: Larger loans, longer terms, rate reduction opportunities
Balanced Pool (DreamPass Score: 640-699)
- Target default rate: 3-5%
- Insurance premium: 1.5%
- Typical APR range: 15-18%
- Features: Standard terms, clear path to Conservative pool
Growth Pool (DreamPass Score: 600-639)
- Target default rate: 6-8%
- Insurance premium: 2.0%
- Typical APR range: 18-22%
- Features: Smaller loans, shorter terms, intensive support
Reality check:
- These ranges are estimates based on traditional lending data
- Actual rates will depend on cost of capital, defaults, regulations
- We may not be able to beat all competitors on rate
- Our advantage is transparency + alignment, not necessarily lowest APR
- Initial loans will likely be at higher end of ranges (risk management)