印尼金融科技p2p借贷(P2PL)的绩效映射

Kaspar Situmorang, H. Siregar, N. Zulbainarni, Roy Sembel
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引用次数: 0

摘要

:印尼p2p借贷(P2PL)金融科技发展迅速。在这种快速增长的过程中,一个不稳定的模式显示了P2PL在其性能方面的动态。本研究旨在绘制金融科技p2p物流的绩效图。所使用的数据是从每家公司的网站上获得的贷款和不良贷款的支付总额以及金融服务管理局(OJK)公布的汇总数据。本研究通过对各平台102家金融科技公司的网站进行抓取,获取不良贷款(NPL)值和累计贷款分布。本研究还采用分层聚类方法,根据不良贷款和累计贷款支出对各p2p物流企业进行分组。在层次聚类分析的基础上,三个聚类区分了分组p2p物流公司的特征。在第一个集群中,有3家公司具有高分布和低不良贷款,而在第二个集群中,有13家公司被归类为表现不佳,因为它们与低支出和高不良贷款值有关。在第三组中,有71家公司的支出和不良贷款适中。基于这种映射,从开发风险开始,有几件事情需要改进
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Performance Mapping Of Fintech Peer To Peer Lending (P2PL) in Indonesia
: The development of Peer-to-Peer Lending (P2PL) fintech in Indonesia was growing fast. In the midst of this rapid growth, a volatile pattern shows the dynamics of the P2PL in terms of its performance. This study aims to map the performance of fintech P2PL. The data used are the total disbursement of loans and non-performing loans obtained from each company's website and aggregate data published by the Financial Services Authority (OJK). In this study, a website scraping from 102 fintech companies was obtained from each platform to obtain Non-Performing Loan (NPL) value and accumulated loan distribution. This study also uses the hierarchical clustering method to group each P2PL based on NPL and accumulated loan disbursement. Based on the hierarchical clustering analysis, three clusters distinguish the characteristics of grouping P2PL companies. In first cluster, there are 3 companies with high distribution and low NPL, while in the second cluster consists of 13 companies categorized as poor performance because they related to the low disbursement and high NPL value. In the third cluster there are 71 companies with moderate disbursement and NPL. Based on this mapping several things need to be improved, starting from developing a risk
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