Supervising Lending Fintech with Big Data Surveillance
IT based lending services, commonly known as lending fintech, is growing rapidly in Indonesia. On the one hand, the emergence of lending fintech is able to reduce financial gap that cannot be fulfilled by banks and other conventional financial institutions. However, on the other hand, many people have become victims of these firms that violated regulations and business ethics.
Generally, as stated in our recent article, “Big Data-Based Peer-to-Peer Lending Fintech: Surveillance System through Utilization of Google Play Review,” those victims suffer under unethical debt collection methods (intimidation, violation of privacy, terror, etc.) and the imposition of excessive interest rates and other charges. Unethical debt collection methods have victimized many people through this dishonorable practice. For example, currently more than 5,000 people have signed an online petition at change.org entitled "Fintech Loan Debt Collection is Very Troubling" which contains complaints to the Indonesia Financial Services Authority (OJK) and other stakeholders.
One example of such bad practice is that an employee was even fired from her job (Tribun Jabar, August 2018). As a result of her late payment of loans from two lending fintech providers, their debt collectors sent messages to all the contacts stored on her cellphone. In fact, the debt collector created a Whatsapp group consisting of her friends, family, and boss only to collect her debt and humiliate her.
Furthermore, there is the imposition of high interest rates and other charges such as penalty and administrative charges, which burden many people. Some of them complain that the interest rate of lending fintechs reaches up to 30% per month.
Urgency of Early Warning System
The OJK specifically has undertaken some actions to deal with these issues. However, in general the supervision carried out by the Authority is still weak and often late because they generally wait for official reports from the public. OJK should be able to be more proactive and take preventive actions. In order to achieve such goal, the Economic Research Center - LIPI (P2E-LIPI) built a big data-based lending fintech surveillance system through utilization of Google Play’s reviews.
By utilizing Google Play’s data of 40,650 reviews from 110 lending fintech applications running from March 3, 2016 to August 28, 2018, P2E-LIPI made an classification of lending fintech based on assessment of four aspects, namely legality, average rating review, negative review related to debt collection method, and negative review related to excessive imposition of interest rates and other charges. The classification consists of 5 categories, A-E, where A represents the best rank that can be obtained if the fintech is registered in the OJK, has an average rating review above 3, and has a reasonable ratio of positive to negative reviews related to debt collection method and imposition of excessive interest rate. In contrast, if a fintech scores poorly in the four aspects then that fintech falls into the E category and the Authority has to take serious action immediately in order to avoid more people becoming victims.
By using methods of relational database, structured query language, and text analysis, the research provides several findings. First, 77% of lending fintech applications found on Google Play are illegal applications meaning that they that are not officially registered in the OJK. Second, the average rating reviews of registered fintechs is better than those of firms that are illegal. Third, there are considerable numbers of negative reviews found on Google Play regarding unethical debt collection methods and imposition of high interest rates.
Furthermore, this system is able to act as an early warning system for the authorities to supervise and monitor lending fintechs considering that data retrieval can be done in real time. In fact, if only this system had been implemented at the beginning of the year, then some cases of victims that emerged and were reported in mass media could have been avoided or at least the number of victims could have been minimized. The backtrace result of this research found that those cases had indeed been detectable earlier, with firm names of those problematic lending fintechs in category E.
Lending fintech runs its business by adopting high technology methods, therefore, the OJK should also use an unconventional surveillance approach through implementation of the system examined in our article. Furthermore, eyeing the vast potential of this system, OJK should implement such big data-based surveillance system to perform its functions to protect consumers optimally. In addition, this system also allows OJK to be more proactive and preventive, without having to wait for reports made officially by consumers, which in most cases will only result in actions by the OJK being taken too late.
Nika Pranata - Economic Research Center, Indonesian Institute of Sciences (LIPI)
Alan Ray Farandy - Economic Research Center, Indonesian Institute of Sciences (LIPI)