Case Study: How a Lending Protocol Utilized Excess Tokens Through Subordination

The Problem: What to Do with Tokens Given to Lenders

A crowdlending platform for small and medium businesses. The model: the platform connects lenders (individuals, average deposit $50–$20,000 USDC) with borrowers (companies, loans of $500K–$1M in $100–$200K tranches). Collateral coverage: 30–50%.

To attract lenders, the platform issues cashback in tokens for every deposit. 65% of total supply (6.5M out of 10M tokens) is allocated for these rewards. The remaining 35% covers team vesting, protocol treasury, and liquidity provision — beyond the scope of this utilization case study. The problem is obvious: if lenders simply sell the tokens they receive, it creates constant sell pressure. Cashback loses value → incentive disappears → lenders leave → the platform loses liquidity.

The task for the tokenomist: design a token utilization mechanism that makes lenders spend their cashback inside the platform rather than sell on the market.

The Solution: Bonding Curve + Subordination + Burn

The solution is built on three components, each reducing sell pressure.

Component 1: Bonding Curve Instead of Fixed Emission

Tokens aren’t distributed on a schedule — they’re minted through a bonding curve only when lenders actually deposit. The mint price rises as a power function:

The average mint price P_average serves as an internal oracle for converting token payments:

Component 2: Three-Tier Subordination — The Primary Token Sink

Each lender receives CT tokens (confirmation tokens) representing their position in the lending pool. By default, all positions have Middle priority. A lender can:

• Upgrade to High — by paying tokens. In case of borrower default, High positions absorb losses last (maximum protection)
• Downgrade to Low — for free, receiving a payout from the upgrade fund. Low positions absorb losses first

This is the key utilization mechanism: lenders spend their cashback to protect their own deposits.

The upgrade price increases linearly as the High bucket fills up (target operating range: up to 1/3 of all positions; the formula allows higher fill with escalating costs):

Payouts to Low-tier lenders are weighted by time to maturity — the longer until repayment, the higher the payout:

In case of default, losses are absorbed in order: Low → Middle → High.

Component 3: Burn Mechanics and Additional Demand

Subordination burn. Example: lenders spend 100 tokens to upgrade to High priority. The platform takes a 20% fee (20 tokens — burned). The remaining 80 tokens go to those who agreed to downgrade to Low. If fewer lenders want to downgrade than upgrade — the unclaimed remainder is also burned.

Quarterly buyback & burn — 10% of the platform’s net profit goes to buying tokens on the market and burning them.

KYB/Due Diligence payments — borrowers pay for verification (~$22,000 USDC equivalent) with a discount when paying in tokens. At 5–10 new projects per month, that’s $110–220K USDC in additional demand.

Late payment penalties — penalty rate of 2–4x the base rate (e.g., at a base rate of 30% APR with a 3x multiplier — 90% APR penalty). Payable in USDC or tokens at P_average.

Calculator: Bonding Curve and Cashback

How to Use the Calculator

• Enter the deposit amount in USDC and the current number of minted tokens
• Set the cashback coefficient (LLC) — the share of deposit returned as tokens
• Set the High bucket fill percentage to calculate upgrade costs
• The calculator shows the bonding curve mint price, cashback amount, and the cost of protecting a position

Impact Analysis: Token Utilization

Token Flow Funnel

Bonding Curve (minting)
    │
    ├─── 80% → Lender receives cashback
    │         │
    │         ├─── Upgrades to High priority → UTILIZATION
    │         ├─── Sells on market → SELL PRESSURE
    │         └─── Holds (loyalty tiers) → RETENTION
    │
    └─── 20% → Affiliate / Platform
                │
                └─── Sells or spends → MARKET

    Subordination (100 tokens spent):
    ├─── 20% platform fee → BURN (20 tokens)
    └─── 80% → payout to Low-tier lenders (80 tokens)
         └─── unclaimed remainder → BURN

    KYB/DD: ~$22K per borrower verification → TOKEN DEMAND
    Penalties: 2-4x base rate → TOKEN DEMAND
    Buyback: 10% of net profit → BURN

Quantitative Assessment

With stable platform operations (assuming 10 pools at $500K USDC each, LLC = 5%):

With active borrower flow, token demand through KYB/DD can exceed cashback emission. But this depends on the rate of new project onboarding. If borrower inflow slows, utilization drops to ~$90K (subordination + buyback) — 36% of emission. The model works as long as:

1. Bonding curve slows emission — at 4M tokens minted, mint price reaches ~$15 (vs. ~$6 at 500K minted), significantly reducing emission per deposit
2. Real defaults drive High demand — if 2–3 pools default in year one, Low-tier lenders lose money, and others rush to upgrade
3. Buyback is tied to real revenue — if the platform generates 3% of loan volume ($150K+/month), burn scales proportionally

Weaknesses

Risks identified in the model

Key Takeaways

What worked in this model

The main lesson: in a lending protocol, token utilization should be tied to risk management, not staking or gamification. A lender spends tokens because without upgrading their priority they risk losing their deposit — that’s fundamentally stronger than “stake and earn APY.”