P2P學生借貸之社群推薦機制

Abstract

P2P 網路借貸（Peer-to-Peer Lending）是金融科技（Fintech）浪潮中的一個趨勢，在網路平台上，個體對個體的直接借貸行為，去除傳統中介-銀行的角色，利用網路平台作為中介業務。而此類網路平台往往受制於資訊不對稱、缺乏良好信用評價機制等困擾，導致貸款人怯於投入資金，甚至出現逆向選擇(adverse selection)。本文以學生借貸為研究環境，提出一種推薦機制，期望借助社會資本與社群之計算，分析貸款人的投資意願、學歷背景相似度、與借款人之間的關係，讓貸款人能夠快速找到合適的借款人；同時可解決傳統學貸金額限制、利率過高之困境。

Peer-to- peer lending is one of the new trends in the field of financial technology. Without the intermediary of financial institution, individual borrowers can directly carry out a loan transaction with individual lender. Traditional banks are no longer the only place where can apply for a loan. Nevertheless, most of peer-to-peer lending platforms lack little opportunity for face to face contact between borrower and lenders, and cannot provide complete credit rating system to evaluate borrower’s identity, which results in the asymmetric information problems and lenders are unwilling to investing money, or even bring about the adverse selection problem. In our research, we take the student loan market as the research target and propose a social recommendation mechanism borrowers based on the theory of social capital and social computing. Through analyzing users’ investment inclination, borrower performance, academic similarity and relationship, we help lenders find suitable borrowers, meanwhile, solve the difficulty that students face in getting a loan in traditional financial institution.