Risk Classification of Peer-To-Peer Lending Platform Using SVM Algorithm

Corry Elsa Noviyanti, Yova Ruldeviyani

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The transition from traditional to digital of financial industry in Indonesia is supported by the role of peer-To-peer lending platform. Technology disruption on the peer-To-peer lending platform goes hand in hand with the high risks that must be faced by the users. In this study, text classification is conducted to find the risks that perceived by the users. Text classification process follows the CRISP-DM data mining process and uses SVM which generates better accuracy compared to the other algorithms. Twitter data of peer-To-peer lending platforms is used in this study and classified based on the perceived risk into six classes, namely performance, financial, time, psychological, social, and privacy. The result of text classification using SVM algorithm generate 81.51% accuracy. Classification results indicate that performance risk is the most felt risk by users and must be considered by peer-To-peer Lending platforms.

Original languageEnglish
Title of host publication2020 International Workshop on Big Data and Information Security, IWBIS 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages29-34
Number of pages6
ISBN (Electronic)9781728190983
DOIs
Publication statusPublished - 17 Oct 2020
Event5th International Workshop on Big Data and Information Security, IWBIS 2020 - Depok, Indonesia
Duration: 17 Oct 202018 Oct 2020

Publication series

Name2020 International Workshop on Big Data and Information Security, IWBIS 2020

Conference

Conference5th International Workshop on Big Data and Information Security, IWBIS 2020
CountryIndonesia
CityDepok
Period17/10/2018/10/20

Keywords

  • CRISP-DM
  • peer-To-peer lending
  • SVM
  • text classification

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