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3Jane Protocol: The Credit-Based Money Market
future@3jane.xyz
v1: February 25, 2025
v1.1: October 19, 2025
Abstract
3Jane is a credit-based money market on Ethereum enabling unsecured lines of credit underwritten
against verifiable proofs of crypto & bank assets, future cash flows, and credit scores. This unlocks
a three-dimensional collateral space within crypto financial markets by introducing future-backed
credit alongside existing asset-backed loans. 3Jane integrates onchain address credit scoring mod-
els via Cred Protocol and Blockchain Bureau with offchain VantageScore 3.0 credit scores via
zkTLS, enabling risk-adjusted underwriting at scale. To maintain protocol solvency, 3Jane oper-
ates onchain auctions where U.S. collections agencies can bid on collecting non-performing debt.
By expanding access to unsecured credit, 3Jane unlocks a new era of capital efficiency, empow-
ering a new class of high-productivity economic actors — including cryptonative sole proprietors,
businesses, and AI agents — to borrow against future productivity.
1 Motivation
Crypto needs credit expansion. The modern capitalist financial system is built on two fundamental
pillars: a medium of exchange & the creation of credit — specifically, the ability to borrow against
creditworthiness and future growth rather than existing assets. The global unsecured credit market
across consumer and commercial segments is estimated at$5.3 trillion. 1 More specifically, cash flow-
based financing in the USA represents a$1.3 trillion market through credit lines, revenue-based loans,
merchant cash advances, and trade credit. 2 While stablecoins have convincingly delivered on the first
pillar over the past decade, DeFi growth remains constricted by the absence of a scalable and capital-
efficient mechanism for credit creation.
DeFi lending is still currently split into two extremes: (1) overcollateralized loans backed by a limited
set of DeFi assets (` a la Aave and Morpho), or (2) uncollateralized loans to offchain private credit firms
(` a la Goldfinch), institutional market makers, and reputation-based social circles (` a la Union). And
despite traditional banks and banking-as-a-service fintech lenders having lower costs of capital under a
fractional reserve system, they remain unable, unwilling, or restricted from underwriting cryptonative
assets and cash flows due to regulatory and risk constraints.
Crypto financial markets need a scalable and permissionless credit primitive that can extend uncollater-
alized credit to cryptonative entities (ie. farmers, traders, businesses, and/or AI agents), underwritten
against the entire universe of DeFi assets, offchain assets, and future assets (ie. cash flows). This not
only unlocks significant capital efficiency and credit limits for existing asset-rich entities, but more
importantly it lays the foundation for enabling high productivity asset-light entities to access credit
against their cash flows, creating a self-sustaining cycle of economic expansion native to the Ethereum
network. To truly become the internet-native financial system — free from bank liquidity — a cryp-
tonative credit primitive must emerge to enable economic expansion backed by future growth.
1Market Research Future.Unsecured Business Loans Market Size.https://www.marketresearchfuture.com/
reports/unsecured-business-loans-market-24673
2Allied Market Research.https://www.alliedmarketresearch.com/small-business-loans-market-A324248
1
Figure 1: 3Jane Protocol
2 Introduction
3Jane protocol is a peer-to-pool credit-based money market enabling algorithmic, real-time uncollat-
eralized USDC credit lines for yield farmers, traders, businesses, and/or AI agents. By leveraging
onchain and offchain credit underwriting, 3Jane introduces a scalable and permissionless credit system
that extends capital to merchants based on verifiable financial proofs rather than posting onchain
collateral. 3Jane expands instant capital access across two key user segments: (1) greater credit lines
and capital-efficiency for asset-rich yield farmers and traders by leveraging their entire financial profile
across all DeFi assets 3, centralized exchanges, brokerage, and bank assets and (2) enables capital
access to high-productivity asset-light businesses and AI agents for working capital and growth financ-
ing across financial markets, service markets, and resource markets, underwritten against their future
cash-flows.
The protocol has three primary functionalities:
1.Core money market: two-sided market where suppliers deposit USDC into the pool to mint
USD3, and optionally stake for sUSD3, and get exposure to a diverse pool of credit lines to
crypto users. On the other side, merchants permissionlessly connect their ETH address and
bank account via Plaid and instantly generate a 0% collateral open-term variable implied apy
USDC credit facility
2.Credit underwriter: 3Jane-operated offchain credit underwriting algorithm3CAthat under-
writes credit lines against all of a user’s verifiable (1) DeFi assets, offchain assets, and future
assets (ie. cash flows) and (2) onchain/offchain credit scores. It derives the credit line amount,
default credit risk premium implied APY on top of the base money market implied rate, and the
repayment rate
3.Credit slasher: 3Jane maintains protocol solvency by implementing two mechanisms for incen-
tivizing repayment: (1) slashing a user’s 3Jane credit score and (2) running an onchain dutch
auction with licensed U.S.-based collections agencies who may bid on collecting the debt on a
contingency basis.
3CEX crypto, stablecoins, majors, alts, NFTs, farms, CDPs, money markets, DEX LP’s, staking, restaking,
vesting/hard-lock, derivatives DEXs, yield swaps (PT/YT), aggregators, insurance LPs, bridge LPs, options LPs, So-
cialFi, R W As, lotteries, and prediction markets across all EVM chains.
2
3 Protocol Architecture
Figure 2: Protocol Architecture
3.1 Core Money Market
3.1.1 Suppliers: USD3 and sUSD3
Suppliers can permissionlessly deposit USDC to mint USD3. USD3 is a stablecoin-denominated yield-
coin (ERC-4626) backed by the pool of credit lines. USD3 has a senior claim on payments from the
pool and has real-time liquidity for withdrawals based on the market reserves.
Suppliers can also permissionlessly 4 stake USD3 for sUSD3 (ERC-4626) which gives holders levered
yield on the pool of credit lines while absorbing first-losses in the case of defaults, net recoveries.
sUSD3 is subordinate debt to USD3 and has a cooldown period before it can be withdrawn for USDC.
All USDC supply is first deposited into the Aave V3 USDC pool, and aETHUSDC is then redeposited
into the core money market. Funds are withdrawn from Aave as 3Jane merchants draw down their
USDC credit lines. This ensures a base Aave money market return on idle capital.
See 3.1.3 for USD3 and sUSD3 APY.
3.1.2 Merchants
All U.S.-based merchants can permissionlessly access 0% collateral USDC credit lines in real-time
within two minutes. The connection flow is as follows:
1.User connects their walletand signs a message verifying ownership
4staking capacity is capped at W sUSD3. See 3.1.3.
3
2.User logs into bank accountvia Plaid. 3Jane retrieves the asset and income responses within
a zkTLS wrapper (zkFetch), with Reclaim acting as the proxy for added proof of provenance
(PoP). 3Jane securely stores asset, income, and user identity. See 3.2.2 for more on zkTLS and
3.2.4 for more on privacy. 3Jane runs the credit underwriting algorithm (3CA) to generate a
user’s USDC credit line sizeCL, credit default risk implied APY IR DRP,n, and repayment rate
RRn. See section 3.2 for more on the 3CA methodology. 3Jane’s credit underwriting module
posts credit line terms onto theCreditLinecontract and the zk proofs and signature on the
zkTLSProofscontract
3.User signs an MCA legal clause messagefor repayment
4.User pulls USDCup to their credit lineCL
Optionally, the user can further boost their credit line and rate by (1) signing more addresses to create
a composite address (2) login to Credit Karma to connect more credit data (3) login to CEX’s and
other banks.
Figure 3: Merchant Connection Sequence
3.1.3 Core
The core money market is an instantiation of the Morpho Blue contract with a single market 5 and the
following augmentations:
1. Collateral position is replaced by thecredit underwritermodule via theCreditLinecontract
2. Liquidation LTVs are replaced with credit slashing logic via thecredit slashingmodule to incen-
tivize repayment and maintain protocol solvency
3. Positions have an additional (1) address-specific credit default risk implied APY (2) market-wide
late penalty implied APY on top of the base IRM implied APY and (3) payment rate
The formula for merchant implied APY’s, total pool implied APY’s, and tranche-specific APY’s and
weights is as follows:
IRn = IRBase + IRDRP,n + 1late,n ·IR LP (1)
IR3Jane = WAave ·IR SOFR +
NX
n=0
Wn ·IR n (2)
5loanToken = aETHUSDC, collateralToken = USDC, oracle = USDC oracle, lltv = 100%
4
WUSD3 ·IR USD3 +W sUSD3 ·IR sUSD3 = IR3Jane (3)
where
IRn = User-specific implied APY
IRBase = 3Jane base variable implied APY based on utilization. Always greater than SOFR
IRDRP,n = 3Jane default credit risk implied APY of merchant
IRLP = 3Jane late penalty implied APY. Applied if delinquent past repayment block timestamp
IR3Jane = Variable composite implied APY of total merchant pool
IRSOFR = Aave’s USDC market secured overnight financing rate
IRUSD3 = Interest rate of USD3 tranche. Subject to protocol risk parameter updates
IRsUSD3 = Interest rate of sUSD3 tranche. Subject to protocol risk parameter updates
WAave = Weight of pool in Aave USDC market
Wn = Weight of outstanding credit line to merchant n
WUSD3 = Fixed at 85% pool weight
WsUSD3 = Fixed at 15% pool weight
3.1.4 IRM
The implied interest rate model is a variant of the AdaptiveCurve IRM which (1) in lieu of interest
rate arbitrageurs enforcing market dynamics, programmatically sets the interest rate at 0% utilization
to be at minimum IR SOFR and (2) makes the curve steepnessCand target utilizationU t adjustable
protocol risk parameters based on credit risk changes due to market conditions.
r(Ut)≥C·IR SOFR
r(0%) = 1C ·r(U t)≥ 1C ×(C·IR SOFR) = IRSOFR
Thus, the relationship ensures:
r(Ut)≥C·IR SOFR,which impliesr(0%)≥IR SOFR
3.1.5 Repayments
Eachmonththe merchant must make the following minimum payment to maintain good standing:
min
LA, P·
IRBase + IRDRP,n
12 + RRn
whereLA= lifetime appreciation, P= outstanding principal
3Jane credit lines are structured as a purchase on future receivables, meaning the required repayment
may not surpass lifetime portfolio growth via asset appreciation and/or cash flows since first drawing
down the credit line 6. Therefore, the merchant must only repay the minimum between lifetime appre-
ciation since drawing down the credit line and the implied APY plus repayment rate.
The merchant has a grace periodG p to repay the debt after the new repayment is triggered. After the
grace period, the merchant enters the delinquency periodD p where they begin accruing an additional
late implied APY IR LP on the outstanding principal. If the credit line has not been repaid before
the delinquency period block timestamp, the credit line enters default status at which point the 3Jane
credit slashingmodule is automatically triggered which initiates an NPL auction. See 3.3 for more.
6asset appreciation is summed up on an hourly basis.
5
3.2 Credit Underwriter
Figure 4: 3Jane Credit Underwriting Algorithm (3CA)
The 3Jane Credit Risk Algorithm (3CA) generates a user’s credit line, credit default risk implied APY,
and repayment rate, underwritten against assets (DeFi, CEX, Bank, Brokerage), cash flows (income,
vote-escrowed tokens, revenues), and creditworthiness 7 (Cred score, Blockchain Bureau score, and
Equifax / TransUnion VantageScore 3.0), and posts it onchain alongside zkTLS proofs.
3.2.1 3CA
Algorithm 13CA
1:procedure3CA(a, u)▷Address and user
2:a composite ←sign(a 1, . . . , an)▷User cryptographically signs addresses 1 to n
3:cred←cred(a composite)
4:bcb←bcb(a composite)
5:ck←ck(u)
6:s←score(cred s, bcbs, ckeq, cktu, ar)▷3Jane score
7:v w, cw ←debank(a composite)
8:v b, cb ←bank(u)
9:v c ←cex(u)
10:v←dvalue(v w, vb, vc, cw, cb)▷3Jane value
11:q←qualify(cred, bcb, ck, s, v)
12:ifqthen
13:IR DRP,a ←drp(s)▷Credit default risk premium
14:RR a ←rr(s)▷Repayment rate
15:CL a ←cl(s, v)▷Credit line
16:return(IR a,RR a,CL a)
17:else
18:return
19:end if
20:end procedure
See figure 6 in the appendix for more detail.
7both Cred protocol and Blockchain Bureau up a robust credit underwriting framework based on on-chain activity.
Combined, both credit scoring services scan over +500m address across 8 EVM chains and +100 DeFi protocols to feed
+1000 features into its scoring algorithms. Model is trained on +1000 features, 54bn transactions, and 1PB of data.
Every transaction you make - from credit line pulls, repayments, liquidation, coins held, age of address, yield generated,
exchanges you interacted with, etc. contributes to a user’s creditworthiness.
6
3.2.2 zkTLS Proofs
3Jane uses Reclaim protocol, which leverages the zkTLS 8 proxy model, in order to fetch and prove
the integrity of HTTPS responses of a user’s VantageScore 3.0 score via Credit Karma, CEX assets,
and Bank cash & other cash flows via Plaid, without introducing additional trust assumptions on the
user or protocol itself. Furthermore, 3Jane utilizes EigenLayer’s cryptoeconomic security to ensure a
collusion resistant set of designated verifiers that scales with credit line sizes. Proofs will be posted
onchain by thecredit underwritingmodule alongside the corresponding data, giving depositors the
ability to audit the health of the pool. This is critical for 3Jane’s architecture for two reasons:
1.Extracting offchain credit data:traditionally, in order to access a user’s credit data the
merchant must (1) provide their social security number (SSN) and (2) the creditor must be
onboarded with 1-2 of the major credit bureaus, after which point the creditor uses your SSN
to do a hard check with the credit bureaus to receive your credit report. 3Jane avoids collecting
SSN’s and hard checks entirely by leveraging zkTLS to trustlessly and privately extract your
credit data directly from your Credit Karma account upon log in without introducing additional
trust assumptions onto the user
2.Selective disclosure of offchain balances:in order to maximize the auditability of the
merchant pool by creditors, 3Jane leverages zkTLS to make onchain proofs about some statement
on the API response, in particular whether the CEX and bank balance isgreater than or equal
to some floorV f
3.2.3 zkCoprocessor Proofs
3Jane leverages Lagrange protocol’s zero-knowledge coprocessor (zkCoprocessor) to prove arbitrary
current or historical state of any EVM chain pertaining to an address, ensuring onchain failsafes for
credit underwriting and turning away flagged sybils, fraud, or uncreditworthy users on the smart
contract level.
3.2.4 Privacy
Realistically, 3Jane doesn’t need to know who you are unless you default. Upon bank connection,
3Jane retrieves yourfull name, email address, phone number, and city/state, which is subsequently
sharded and encrypted across multiple cloud providers. Data is retrieved only if a user is delinquent
on their credit line repayment. This approach strikes a balance between collecting minimal viable
personal data and maintaining effective collection strategies by outsourcing skip tracing to licensed
collections agencies with access to commercial databases via TLOxp 9. The user will have the option
to delete their data once the credit line is repaid, with the option of reconnecting their bank account.
8zero-knowledge TLS (zkTLS) allows one to obtain and prove the provenance of arbitrary HTTPS traffic, and without
revealing personal identifiable information associated with that HTTPS session.
9TLOxp is a powerful skip tracing and debt recovery tool that equips licensed collections agencies with 100 billion
public and proprietary data points that helps agencies locate debtors and verify identities.
7
3.3 Credit Slasher
Figure 5: NPL Auction and Repayment
Thecredit slashingmodule executes three strategies for deterring defaults, namely (1) slashing the
3Jane score which decreases future credit line sizes and increases future implied APY (2) pooled upside
model where late payments from defaulters is distributed pro-rata across all existing merchants (3)
initiating a non-performing credit line (NPCL) auction which engages collections agencies.
3.3.1 NPCL Auction
3Jane initiates an onchain dutch 10 auction for licensed U.S.-based collections agencies 11 to bid on
collecting the debt on a contingency basis, meaning if they are successful in collecting they get to keep
a shareR c of the principal repaid. The lifecycle is as follows:
1. Auction is triggered by delinquency and 3Jane shares merchant profile with all onboarded collec-
tions agencies, including onchain/offchain credit data, assets, and cash flows. Does not include
PII
2. The contingency rateR c increases over time until an agency accepts. 3Jane shares private user
data with the particular collections agency
3. Agencies proceed to collect on the debt via skip tracing. These agencies have multiple means
of incentivizing repayment, including the right to furnish defaults to offchain credit bureaus to
slash offchain credit scores and increase future credit exclusion, and get a court injunction to
force repayment
4. If successful, merchant directly repays outstanding balance onchain. P·(1−R c + IRn −IR LP) is
sent back into the money market, P·R c is routed to the collections agency, and P·IR LP is split
pro-rata across all existing merchants
3.3.2 Debt W rite-Off
When a credit line becomes delinquent, its market value should reflect both the probability of loss and
the probability of recovery. Instead of an immediate full markdown to zero, we introduce a model which
dynamically adjusts the markdown based on the probability-weighted expected recovery. This ensures
that the markdown reflects real-world expectations while preserving the option for future recoveries.
The total adjusted market valueM(t) of a delinquent credit line is given by:
10dutch auctions ensure rapid price discovery, which is essential for quickly offloading NPLs. Collection agencies will
only bid when they believe they can profitably recover a portion of the debt.
11these agencies specialize in collections for merchant cash advances
8
L(t) = 11 +e −k(t−tc) (4)
R(t) = Rmax
1 +e kr(t−tr) (5)
M(t) = (1−L(t)) +R(t) (6)
where
t= Time in days since delinquency began
tc = Half-life (time when the credit line is 50% marked down)
tr = Time threshold beyond which recoveries significantly decline
k= Steepness of the markdown curve
Rmax = Maximum expected recovery rate
L(t) = Loss probability markdown
R(t) = Expected recovery rate
M(t) = Market value multiplier
This model ensures an accurate valuation of non-performing credit lines while maintaining protocol
solvency and preventing market panic from sudden markdowns to zero. This function ensures that at
the early stages of delinquency, recoveries are still probable, reducing the markdown severity. How-
ever, as delinquency time increases, the likelihood of successful recovery diminishes. By dynamically
adjusting for expected recovery, it provides a more nuanced and realistic approach to loss recognition.
Credit lines are pre-emptively marked down to 0 cents on the dollar upon delinquency status in order
to disincentivize runs on the money market.
3.4 Default Game Theory
One of Ethereum’s superpowers is around the idea of game warping — the ability to alter the game
theory equilibrium by ensuring perpetual credible commitments onchain through code. Turns out,
unsecured credit is the perfect application for this — b프로젝트로 돌아가기
