Why Algorithmic Credit Must Learn From Traditional Finance's Mistakes

UST and LUNA's death spiral was a crypto-native Minsky Moment. Using a protocol's own token as primary collateral creates reflexive, pro-cyclical risk.\n- Reflexivity: Price drops trigger liquidations, which crash price further.\n- Correlation: During a market crash, all correlated assets (e.g., governance tokens) fail simultaneously.\n- Lesson: Truly exogenous, non-correlated collateral is non-negotiable.

Maker's evolution from pure-crypto to incorporating Real-World Assets (RWAs) is the canonical playbook. It diversifies collateral away from volatile crypto assets.\n- RWA Vaults: Tokenized T-Bills and corporate credit provide yield and stability.\n- Stability Fee Mechanism: Acts as a central bank, adjusting borrowing costs to manage DAI supply.\n- Result: ~$3B+ in RWA collateral now underpins DAI, providing a non-correlated yield base.

TradFi's CDO-squared crisis was hidden leverage. In DeFi, it's recursive lending and composable leverage via protocols like Aave and Compound.\n- Nested Positions: Borrowing one asset to collateralize another creates hidden systemic exposure.\n- Oracle Risk: A single price feed failure can cascade across multiple protocols.\n- Contagion: The insolvency of one large, leveraged position (e.g., 3AC) threatens the entire credit system.

DeFi's superpower is transparent ledger data. Protocols like Gauntlet and Chaos Labs run real-time simulations to set safe risk parameters.\n- Dynamic Risk Parameters: Loan-to-Value ratios and liquidation thresholds adjust based on volatility.\n- Protocol-Wide Debt Ceilings: Hard caps per collateral asset prevent over-concentration.\n- Example: Aave's Safety Module and risk-adjusted capital efficiency metrics.

TradFi's 2008 run on money market funds has a direct analog: the de-pegging of USDC or DAI during a bank crisis. Liquidity vanishes when it's needed most.\n- Concentrated Deposits: Over-reliance on a few centralized entities (e.g., SVB for USDC).\n- Stablecoin De-Pegs: Trigger mass redemptions and cripple lending protocol liquidity.\n- Result: Borrowers face instant liquidation even if their collateral is sound.

The iron rule returns: Always over-collateralize. Protocols must enforce higher safety margins and diversify stablecoin dependencies.\n- Multi-Collateral DAI: Not reliant on any single stablecoin or asset.\n- Elevated Collateral Ratios: 150%+ minimums for volatile assets to absorb black swan volatility.\n- Protocol Design: Isolate risk silos; a de-peg in one asset pool shouldn't bankrupt the entire system.

Pure algorithmic models like Terra's UST or Iron Finance collapsed because their stability depended on their own token's price. This creates a reflexive feedback loop where a price drop triggers more issuance, accelerating the crash.

• No exogenous collateral to absorb sell pressure.
• Oracle reliance becomes a single point of failure during volatility.
• Design assumes perpetual growth, ignoring contraction phases.

Maker learned from 2018's ETH crash and 2020's Black Thursday by moving from single-collateral to a diversified, risk-parameterized vault system. It mimics a central bank's balance sheet management.

• Debt Ceilings limit exposure to any single asset (e.g., $750M for USDC).
• Stability Fees and Liquidation Ratios are tuned per collateral type.
• Surplus Buffer (Pause) of ~250M DAI acts as a first-loss capital reserve.

Protocols like Maple Finance (in its first iteration) and TrueFi faced runs when loan defaults concentrated in a few large, undercollateralized positions. Their pools suffered from a classic bank run problem: liquid liabilities (redeemable lender tokens) vs. illiquid assets (term loans).

• Overcollateralization was social, not enforced.
• No dynamic provisioning for expected losses.
• Liquidity crunches when lenders exit en masse.

Goldfinch applies a first-loss capital structure from TradFi securitization. Junior tranche backers absorb initial defaults, protecting the senior pool. This explicitly prices risk and aligns incentives.

• Junior Capital provides a ~10-20% loss buffer.
• Audited, real-world borrower financials required.
• Yield waterfall ensures senior pool gets paid first, mimicking bond covenants.

Many credit protocols are black boxes where loan approval is a governance vote or a multisig decision. This lacks scalability, consistency, and creates centralization risks. It's the equivalent of a bank with no credit committee or documented lending policy.

• Human latency slows down capital deployment.
• Governance attacks can approve malicious loans.
• No reproducible risk model for lenders to audit.

Spectral and Cred Protocol are building MACRO Scores—non-transferable, composable credit NFTs based on wallet transaction history. This automates and standardizes underwriting, moving towards a decentralized FICO score.

• Algorithmic scoring using 650+ on-chain data points.
• Score is soulbound, preventing Sybil attacks via NFT trading.
• Composable primitive for other protocols to set risk-based rates.