  Online Peer to Peer Lending (P2PL) systems connect lenders and borrowers
directly, thereby making it convenient to borrow and lend money without
intermediaries such as banks. Many recommendation systems have been developed
for lenders to achieve higher interest rates and avoid defaulting loans.
However, there has not been much research in developing recommendation systems
to help borrowers make wise decisions. On P2PL platforms, borrowers can either
apply for bidding loans, where the interest rate is determined by lenders
bidding on a loan or traditional loans where the P2PL platform determines the
interest rate. Different borrower grades -- determining the credit worthiness
of borrowers get different interest rates via these two mechanisms. Hence, it
is essential to determine which type of loans borrowers should apply for. In
this paper, we build a recommendation system that recommends to any new
borrower the type of loan they should apply for. Using our recommendation
system, any borrower can achieve lowered interest rates with a higher
likelihood of getting funded.
