Recommendation Engine for Lower Interest Borrowing on Peer to Peer Lending (P2PL) Platform

Autor:	Ren, Ke, Malik, Avinash
Rok vydání:	2019
Předmět:	Quantitative Finance - General Finance Computer Science - Machine Learning Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted in Web intelligence 2019, this is a long version
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1907.11634 Zobrazit plný text záznamu View this record from Arxiv