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

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.