Increase Collateral Factor of IOTX to 45%

Summary

To increase the collateral factor of (BSC peg) IOTX to 45%.

For

Increase collateral factor of (BSC peg) IOTX to 45%

Against

"Do nothing, remain at current 0%

Motivation

To increase the usage of IOTX as collateral for Cream Finance and help to increase and attract new users and capital, especially on BSC. Currently, the collateral factor is set to 0% and IoTeX users are unable to borrow against the collateral.

IOTX has a daily trading vol of 10m USD on Binance and BSC and has a great community of 50K users, which will generate organic lend/borrow requirements for CREAM.

Additional Info

Starting out as an open-source project in 2017, IoTeX (iotex.io) has built a decentralized platform whose aim is to empower the open economics for machines — an open ecosystem where people and machines can interact with guaranteed trust, free will, and under properly designed economic incentives.

With a global team of over 40 research scientists and engineers behind it, IoTeX has built their EVM-compatible blockchain from scratch using the innovative Roll-DPoS consensus and launched in 2019 April, which has been running by 100+ delegates worldwide and has processed more than 10 million transactions already. On top of the IoTeX blockchain, the team has built the essential blocks of infrastructures to connect with Ethereum, BSC, and Heco blockchains such as ioPay wallet and ioTube bridge which serve ten thousands of users. IoTeX helps EVM-based DApps scale without concerning expensive gas fees!

In addition, middlewares such as Decentralized Identity, Confidential Computing, and Secure Hardware are built on top of IoTeX blockchain to enable self-sovereign devices such as Ucam and real-world oracle such as Pebble. The former has been deployed to 3000+ households (http://iott.network/) and still grows rapidly, while the latter has been launched to 300+ developers initially and enables innovative Dapps that connect the physical world with the crypto world such as real-world NFTs, weather derivatives, and machine learning-as-mining.