Swaylend Economic Risk Assessment Report

Overview

At Chainrisk Labs, we specialize in simulation-based risk management for DeFi protocols. Our recent collaboration with Swaylend — a decentralized lending platform built on FuelVM — showcases the power of rigorous data-driven analysis in enabling sustainable growth.

This audit wasn't just a checkup — it was a thorough review of Swaylend's economic engine, with real recommendations tailored to its liquidity, volatility, and user dynamics.

What is Swaylend?

Swaylend is a fork of Compound V3, rewritten in the Sway language to operate on FuelVM. It introduces isolated lending markets where users can borrow or lend a base asset like USDC while using a range of crypto assets—such as ETH, sDAI, ezETH, weETH, wstETH, FUEL, and USDT—as collateral.

Like any borrowing protocol, its stability relies heavily on carefully tuned risk parameters: collateral factors, liquidation mechanics, interest rate curves, and more. Misalignments here can lead to systemic risks, poor capital efficiency, or even insolvency during extreme market moves.

The Chainrisk Audit Process

We prioritized simulation-driven modeling as the starting point for evaluating Swaylend’s risk architecture.Our proprietary methodology integrates:

Historical data modeling
Percentile-based volatility analysis
Scenario-based stress testing
Agent-based Simulations
Supply/borrow market curve analysis

This approach ensures that recommendations are not based on assumptions but grounded in on-chain data, liquidity trends, and adverse event simulations.

Key Findings

Our analysis revealed varying degrees of volatility and liquidity across supported assets:

Stable assets like USDT and sDAI showed minimal fluctuation, validating higher collateral ratios.
Ethereum and its LSD derivatives (ezETH, weETH, wstETH) exhibited strong correlation and moderate volatility.
$FUEL, while native to the network, presented substantial volatility swings, with drawdowns as steep as ~30% in edge scenarios.

These observations informed our asset-specific risk parameter tuning.

Optimizing the Risk Parameters

Swaylend’s Current Risk Parameters

Collateral Asset	Collateral Factor (%)	Liquidation Factor (%)	Liquidation Penalty (%)
ETH	60	80	20
ezETH	60	75	25
USDT	90	95	5
sDAI	90	95	5
weETH	60	75	25
wstETH	60	75	25
FUEL	40	75	25

Chainrisk Labs’ Recommended Risk Parameters

Collateral Asset	Current Supply	Recommended Supply Cap	Collateral Factor (%)	Liquidation Factor (%)	Liquidation Penalty (%)
ETH	558.6684 ETH	630 ETH	77	82	20
ezETH	184.4383 ezETH	200 ezETH	77	82	20
USDT	195,740 USDT	210 USDT	95	97	3
sDAI	105.6642 sDAI	115 sDAI	91	94	10
weETH	50.8328 weETH	55 weETH	77	82	20
wstETH	4.1836 wstETH	5 wstETH	77	82	20
FUEL	20,400,000 FUEL	40,000,000 FUEL	58	65	15

Interest Rate Curve Comparison: Current vs. Recommended

Parameter	Swaylend Current	Chainrisk Recommendation
Supply Kink	0.8 (80%)	0.7 (70%)
Borrow Kink	0.8 (80%)	0.7 (70%)
Supply Slope Low	1,150,000,000	1,744,038,559
Supply Slope High	70,000,000,000	63,419,583,967
Supply Base Rate	0	0
Borrow Slope Low	1,950,000,000	2,061,136,478
Borrow Slope High	90,000,000,000	79,274,479,959
Borrow Base Rate	775,000,000	792,744,799
Target Reserves	1,000,000	1,000,000
Storefront Price Factor	60%	60%

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Major improvements over existing params:-

Earlier Kink (70%) Instead of 80%
‍By lowering the kink point from 80% to 70%, the protocol begins to penalize over-utilization sooner. This protects liquidity during surges in borrowing and reduces the risk of liquidity crunches during market stress.
Sharper Pre-Kink Borrow Rates
‍Borrow slope below the kink is higher in the recommendation, ensuring better reserve growth and signaling higher cost for sustained borrow usage—critical in volatile scenarios like on Fuel.
Gentler High-End Slopes
‍While Swaylend’s current post-kink slope is extremely aggressive (especially for borrowers), our recommended values ease that slope slightly. This avoids rate spikes that could lead to sudden mass liquidations or discourage necessary borrowing during high volatility.
Improved Supply Participation
‍The adjusted low-end supply slope makes lending more attractive even at low utilization levels, helping grow liquidity faster — without relying on aggressive incentives or liquidity mining.
Better Reserve Growth Structure
‍By raising both the borrow base and early borrow slopes slightly, reserve growth improves incrementally from the start, ensuring long-term sustainability without waiting for high utilization.

How Rates Change with Utilization:

Utilization (%)	Borrow APR (%)	Supply APR (%)	Reserve Growth (%)
0	2.50	0.00	2.50
10	3.15	0.55	2.60
20	3.80	1.10	2.70
30	4.45	1.65	2.80
40 (Current)	5.10	2.20	2.90
50	5.75	2.75	3.00
60	6.40	3.30	3.10
70 (Kink Point)	7.05	3.85	3.20
80	32.05	23.85	8.20
90	57.05	43.85	13.20
100	82.05	63.85	18.20

Why This Model Works

Before 70% utilization: Borrow rates increase gradually, making it attractive for users to borrow. This helps build protocol activity without making it risky.
After 70%: Borrow rates spike sharply, discouraging excessive usage and protecting liquidity for lenders.
Supply rates also rise in tandem, rewarding depositors and keeping the lending side attractive.
Reserve growth increases at higher utilization, ensuring Swaylend builds a buffer during peak demand.

Key Insights from the Comparison

Higher Collateral Factors: Our recommendations increase collateral factors for most assets, allowing users to borrow more against their supplied collateral, thereby improving capital efficiency and making Swaylend more competitive.
Tighter Liquidation Thresholds: Raising liquidation factors further protects the protocol by ensuring timely liquidations, reducing the risk of bad debt.
Reduced Liquidation Penalties: For stablecoins and major assets, we suggest lower penalties, encouraging responsible borrowing and minimizing user losses during liquidation.
Supply Caps Introduced: Our framework introduces recommended supply caps to prevent overexposure to any single asset, maintaining protocol balance and security.
Tailored Adjustments: Each parameter is tailored to the specific volatility, liquidity, and risk profile of the underlying asset, based on rigorous statistical analysis and scenario testing1.

A Framework for Ongoing Resilience

Each parameter was tested using agent-based simulations mimicking flash crashes, low-liquidity slippage, and sudden liquidation events. For instance, our percentile analysis revealed that 40th percentile log returns for FUEL touched −33%, a clear indicator for conservative borrow thresholds.

Final Thoughts

The current set of recommended parameters has been determined with the current liquidity levels in Fuel DEX in mind. However, we note that the liquidity situation on Mira is critical. To mitigate the heightened risk of bad debt, we strongly recommend a significant increase in liquidity for each supported asset in Mira. Without this, the protocol remains exposed to severe financial risk and potential insolvency scenarios.Given the dynamic nature of user and market behavior, ongoing parame- ter evaluation and adjustment are crucial. In addition, Swaylend needs to implement a continuous regime of regular audits, incorporate community feedback, and adapt to evolving market conditions to ensure ongoing stability and resilience.

📈 Want to secure your lending or DeFi protocol with Chainrisk’s simulation-based risk audits? Reach out to us at chainrisk.xyz.

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