Updated daily based on real ERC20 market data
Payship is a major step forward in crypto lending: Deposit any ERC20 token. Use any ERC20 token as collateral. Earn interest on any deposit. Borrow any ERC20 token. Stake PSHP to earn portion of Payship income. Vote with PSHP on Payship future.
Payship is a major step forward in lending economics: Collateral factors and interest rates of deposits and borrows are re-calculated once a day based on the performance of the entire Ethereum ERC20 token ecosystem.
Payship is a major step forward in borrower protection: Every rate has a 24 hour guarantee through the single re-calculation once a day. Every borrower found under liquidation after the re-calculation has 24 hours to fix the position before being liquidated on the next re-calculation.
Off-chain Systems Model (refer to the Whitepaper) enables automatic borrower protection from liqudiation in case of a price increase on borrowed asset or price deacrese on collateral: in case of a borrower going below the liquidation treshold, Payship can automatically and for free repay a portion of the loan using the collateral.
Payship is a major step forward in lender protection: Upon every deposit the lender instantly receives back to the wallet the equivalent value in Payship platform ERC20 stablecoin, the PSD (Payship Standard Denominator). The PSD is unique because while it aims to maintain a peg to the dollar on the open market, within the Payship platform it maintains a peg to each lender's deposit.
It means within the Payship platform the value of PSD will be different for each lender. This also means that the lender has a choice in the event the deposit value changes due to price fluctuations of the underlaying token: use the initial deposit value denominated in PSD in lender's wallet and forego the original token, or swap PSD and take the original token out.
Payship treats privacy & safety of its team and its customers very seriously. To maintain customer privacy, this service does not collet user data, logs or ip numbers. To maintain team security, the above pictures have been digitally manipulated as described: each team member has taken about 300 "selfies" that were supplied to the face generation algorithm to train on top of the base set. The result was further obscured using photo editing software: a depth of field blur was applied to distort details outside the face, and a dust filter was applied to introduce fuzzy pixels complicating photo reuse in other media. The result are photos that look alike, but not exactly like that person.
