The problem xRefinance solves

Let's imagine Alice is there, and she is supporting protocol A. Another protocol B is now available, offering the user improved features, prices, and increased security. It should be clear that the user would want to switch completely or partially to the new protocol B.

Yet, this procedure is tiresome and expensive in gas. Alice must carry out the following actions:

1. She must first pay back the procedure A loan.
2. She must then remove the collateral that was deposited using process A.
3. She must then provide the protocol B collateral.
4. Lastly, she must use protocol B to borrow the debt token.

Due to the numerous transactions required, this process is obviously tedious and time-consuming. Now, Alice might decide against doing this action or would be at a loss if she did so on a network like ethereum, where gas costs are relatively high. Now, imagine that she wishes to move her position to a different chain. That would be tedious.

What if Alice could get an interface where she could just get a single click interface for all the above steps. In addition to making these steps in one-click, it would save a lot of gas too. Perks on perks!!

This is what xRefinance aims to do — providing user with an interface with single click cross-chain refinance with optimized gas costs and much more.
The project uses bridge aggregators to transfer the position data from one chain to another using which positions are created on dest chain and the liquidity on both the chains through the lending system assists in opening new position.

The project also automatically refinances the user's position based on his preferences. What this means is that, if the position of the user gets risky (let's say the health factor falls in the range of risky health factor + 0.1% for aave, (debt/collateral ratio is used to keep track of all protocols) ) then the user's position will be automatically refinanced without the need for user to manually update them.

Challenges I ran into

I used machine learning models to suggest a user refinance options, there is no dataset available for training and testing, as a result data had to be prepared manually.
There were some challenges while implementing automatic refinance as it depends on a number of factors.