Big Data-Based Peer-to-Peer Lending Fintech: Surveillance System Through the Utilization a of Google Play Review

Abstract

Peer-to-peer lending (P2PL) FinTech is growing rapidly in Indonesia. With its flexibility and simplicity, P2PL reduces the gap in financing that cannot be fulfilled by banks. However, the rapid development of P2PL also raises a number of problems that burden users such as unethical debt collection methods, and the imposition of excessive interest rate and other costs that potentially threaten national financial system stability. Therefore, by utilizing big data, which in this case is 40,650 reviews from 110 P2PLs obtained from Google Play within the period of March 2016–August 2018, this research builds a big data-based P2PL surveillance system based on four aspects, namely legality, review rating, debt collection methods, and level of interest rates and other costs. By using relational database, structured query language (SQL) and text analysis, we made several findings: (i) the majority of P2PL in Google Play are unauthorized; (ii) on average, authorized P2PL receives a better review rating; (iii) there are a lot of negative reviews related to unethical debt collection methods and excessive imposition of interest rate; and (iv) there are four P2PLs that required special supervision from the Indonesia Financial Service Authority (OJK). Furthermore, in accordance with the findings, the OJK should not passively wait for official reports to be filed by the public regarding violations of P2PL businesses. Through this big data-based system, the OJK can proactively find these violations, given that the system is proven to be able to act as an early warning system for the OJK in terms of P2PL surveillance.