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

Corry Elsa Noviyanti; Yova Ruldeviyani

doi:10.1109/IWBIS50925.2020.9255512

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

Corry Elsa Noviyanti, Yova Ruldeviyani

Department of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

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 language	English
Title of host publication	2020 International Workshop on Big Data and Information Security, IWBIS 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	29-34
Number of pages	6
ISBN (Electronic)	9781728190983
DOIs	https://doi.org/10.1109/IWBIS50925.2020.9255512
Publication status	Published - 17 Oct 2020
Event	5th International Workshop on Big Data and Information Security, IWBIS 2020 - Depok, Indonesia Duration: 17 Oct 2020 → 18 Oct 2020

Publication series

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

Conference

Conference	5th International Workshop on Big Data and Information Security, IWBIS 2020
Country/Territory	Indonesia
City	Depok
Period	17/10/20 → 18/10/20

Keywords

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

Access to Document

10.1109/IWBIS50925.2020.9255512

Cite this

Noviyanti, C. E., & Ruldeviyani, Y. (2020). Risk Classification of Peer-To-Peer Lending Platform Using SVM Algorithm. In 2020 International Workshop on Big Data and Information Security, IWBIS 2020 (pp. 29-34). Article 9255512 (2020 International Workshop on Big Data and Information Security, IWBIS 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IWBIS50925.2020.9255512

@inproceedings{0308103e41c34ed1aaec9e6fe6ba06de,

title = "Risk Classification of Peer-To-Peer Lending Platform Using SVM Algorithm",

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. ",

keywords = "CRISP-DM, peer-To-peer lending, SVM, text classification",

author = "Noviyanti, {Corry Elsa} and Yova Ruldeviyani",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; 5th International Workshop on Big Data and Information Security, IWBIS 2020 ; Conference date: 17-10-2020 Through 18-10-2020",

year = "2020",

month = oct,

day = "17",

doi = "10.1109/IWBIS50925.2020.9255512",

language = "English",

series = "2020 International Workshop on Big Data and Information Security, IWBIS 2020",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "29--34",

booktitle = "2020 International Workshop on Big Data and Information Security, IWBIS 2020",

address = "United States",

}

Noviyanti, CE & Ruldeviyani, Y 2020, Risk Classification of Peer-To-Peer Lending Platform Using SVM Algorithm. in 2020 International Workshop on Big Data and Information Security, IWBIS 2020., 9255512, 2020 International Workshop on Big Data and Information Security, IWBIS 2020, Institute of Electrical and Electronics Engineers Inc., pp. 29-34, 5th International Workshop on Big Data and Information Security, IWBIS 2020, Depok, Indonesia, 17/10/20. https://doi.org/10.1109/IWBIS50925.2020.9255512

Risk Classification of Peer-To-Peer Lending Platform Using SVM Algorithm. / Noviyanti, Corry Elsa; Ruldeviyani, Yova.
2020 International Workshop on Big Data and Information Security, IWBIS 2020. Institute of Electrical and Electronics Engineers Inc., 2020. p. 29-34 9255512 (2020 International Workshop on Big Data and Information Security, IWBIS 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Risk Classification of Peer-To-Peer Lending Platform Using SVM Algorithm

AU - Noviyanti, Corry Elsa

AU - Ruldeviyani, Yova

PY - 2020/10/17

Y1 - 2020/10/17

N2 - 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.

AB - 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.

KW - CRISP-DM

KW - peer-To-peer lending

KW - SVM

KW - text classification

UR - http://www.scopus.com/inward/record.url?scp=85097628749&partnerID=8YFLogxK

U2 - 10.1109/IWBIS50925.2020.9255512

DO - 10.1109/IWBIS50925.2020.9255512

M3 - Conference contribution

AN - SCOPUS:85097628749

T3 - 2020 International Workshop on Big Data and Information Security, IWBIS 2020

SP - 29

EP - 34

BT - 2020 International Workshop on Big Data and Information Security, IWBIS 2020

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 5th International Workshop on Big Data and Information Security, IWBIS 2020

Y2 - 17 October 2020 through 18 October 2020

ER -

Noviyanti CE, Ruldeviyani Y. Risk Classification of Peer-To-Peer Lending Platform Using SVM Algorithm. In 2020 International Workshop on Big Data and Information Security, IWBIS 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 29-34. 9255512. (2020 International Workshop on Big Data and Information Security, IWBIS 2020). doi: 10.1109/IWBIS50925.2020.9255512

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

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this