What is an interest rate model, and how does it differ from what people typically think of as bank interest rates?
An interest rate model is the mathematical formula within a lending protocol that automatically determines deposit and borrowing interest rates. This formula typically uses a pool's 'utilization rate' as its core input variable — utilization rate refers to the proportion of funds already borrowed out of a pool relative to the total amount deposited. When utilization rises (indicating strong borrowing demand and less available capital left in the pool), the formula automatically pushes up both borrowing and deposit rates; when utilization falls, rates fall accordingly. This entire adjustment process is executed entirely automatically by code, with no one manually setting or adjusting numbers behind the scenes.
The key difference from what people typically think of as bank interest rates lies in the determination mechanism: bank interest rates are usually decided by a combination of central bank policy rates, the bank's internal risk assessment, and regulatory requirements, carrying a considerable degree of human discretion and lag, with adjustment frequency possibly once every few weeks or months; a lending protocol's interest rate model, by contrast, is purely algorithm-driven — in theory it can recalculate rates every block based on current utilization, reacting far faster than a traditional banking system, and it's also fully transparent and public, letting anyone verify exactly how this formula works.
Why does an interest rate model exist, and what problem is it trying to solve?
In traditional banking, the interest rate determination process involves substantial human judgment and internal review processes — a reasonable way for a centralized institution to operate, but entirely unsuitable for a decentralized, permissionless protocol anyone can deposit into or borrow from at any time. If rates needed to be set manually, that would imply the need for a centralized decision-maker, which directly conflicts with a decentralized lending protocol's original design intent. At the same time, a lending protocol's pool scale and borrowing demand can swing dramatically within a short period (say, a major market event causing a large number of users to want to borrow simultaneously) — if rates can't react instantly to this kind of change, a pool could face the risk of funds being instantly drained without enough rate incentive to attract new depositors replenishing liquidity.
What an interest rate model aims to solve is exactly this problem: replacing human decision-making with a fully automated, publicly transparent mathematical formula, letting rates instantly reflect a pool's current supply-and-demand condition — the higher the utilization, the scarcer the capital, and pushing rates up attracts more depositors to join while simultaneously suppressing some borrowing demand — a self-correcting market mechanism requiring no centralized role to intervene in judgment.
How does an interest rate model actually work, and what does the formula look like?
The most common design is a 'kinked interest rate model,' whose core logic sets an 'optimal utilization' threshold (usually between 80% and 90%), with the slope at which rates rise being entirely different below versus above this threshold:
What a depositor actually receives is usually the borrowing rate multiplied by the utilization rate (since depositors earn the interest borrowers pay, minus a portion the protocol takes as reserve or protocol revenue), so borrowing and deposit rates move together with utilization, though deposit rate is usually lower than borrowing rate — the gap between the two is the protocol's or reserve mechanism's revenue source.
What's the practical impact of an interest rate model on everyday users, and what should they watch for?
If you're a depositor, understanding the interest rate model helps you judge whether a pool's currently displayed high deposit rate is sustainable — if utilization is already close to or exceeding the optimal threshold, the rate is high precisely because pool liquidity is tight, and this high rate is to some extent earned in exchange for the risk that 'your funds might temporarily be unable to be withdrawn promptly'; if utilization is far below the threshold, the rate might be lower, but liquidity is relatively ample, and the practical risk faced when withdrawing is also lower. Checking a pool's live utilization figure usually reflects the risk condition you're actually taking on far better than just looking at the displayed deposit APY.
If you're a borrower, understanding the model's slope design helps you estimate the potential range of borrowing cost fluctuation — if you plan to maintain a borrowing position long-term, you need to consider that if overall market borrowing demand suddenly heats up and utilization spikes into the steep range, your borrowing rate could surge dramatically within a short period. This is fundamentally different from a traditional bank loan rate's relatively stable, slow-moving nature, and is a cost volatility risk worth extra attention when using a decentralized lending protocol.
Aave and Compound are among the earliest lending protocols to widely adopt the kinked interest rate model — both set an optimal utilization threshold at a specific point (usually falling between 80%–90% depending on asset type), and publicly display a complete curve chart showing how rates change with utilization, letting users intuitively see how steeply rates spike once utilization crosses that threshold. This design was subsequently adopted by most mainstream lending protocols, becoming the industry-standard approach for decentralized lending interest rate models.
The advantage is that rates instantly reflect the pool's supply-and-demand condition, fully automated and publicly transparent, requiring no centralized decision-maker, and able to self-correct through steep slope design when utilization runs too high, lowering liquidity depletion risk; the drawback is that rate volatility can be far more dramatic than a traditional banking system's, with borrowers potentially facing sharp rate-hike cost pressure within a short period, and depositors needing to actively understand the utilization state to judge whether a displayed high rate comes with corresponding liquidity risk.