Recommendation Engine for Lower Interest Borrowing on Peer to Peer Lending (P2PL) Platform
This addresses a gap in recommendation systems for borrowers on P2PL platforms, though it appears incremental as it adapts existing recommendation concepts to a new user group.
The paper tackles the problem of helping borrowers on Peer to Peer Lending platforms choose between bidding and traditional loans to lower interest rates, resulting in a recommendation system that enables borrowers to achieve reduced interest rates with a higher chance of funding.
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.