Consumer Legal Protection Principles of Billing Mechanism by Digital Financial Service Provider: a Prescriptive Fintech Law Study in Indonesia

Financial Technology or Fintech currently grows rapidly in Indonesia. The total of loan disbursement reached 155,902.55 Million IDR in the end of 2020. Alongside with it‟s development this digital tool like two blades which has negative and positive aspects. This research tries to explore the philosophy of consumer protection in online lending practices and investigate the consumer protection in the online loan collection mechanism by digital financial service providers in Indonesia. Through using some theory and literatures such as the justice theory, consumer protection, economic and law morality, digital document and supported by several primary and secondary legal materials to explain and answer the research question. The research method is the prescriptive by combining the types of doctrinal, reform-oriented, and theoretical research. The results obtained by the author found similarities between dispute resolution from the perspective of the consumer protection act with the arbitration protection law and alternative dispute resolution which both provide legal certainty to the parties

Know Where to Invest: Platform Risk Evaluation in Online Lending

Practice- and policy-oriented abstract for “Research Spotlights” Although enjoying rapid development, online lending also endures some unusual risk, that is, platform risk. We address a new problem at the macro platform level, platform risk evaluation, and explore types of information and methods that are effective in predicting platform risk. We identify four types of information, that is, platform characteristic, risk management, commercial competition, and online word of mouth, and examine their utilities, separately and jointly, in predicting platform risk. We also propose the use of survival analysis, especially the mixture survival model, in predicting whether and when a platform will default. We carry out a cross-stage analysis using data crawled from two leading web portals for online lending in China with the two stages separated by the recent dramatic policy intervention. The results reveal the differences among the four identified factors in terms of predictive utility, the heterogeneity between the two types of default platforms, and differences between the start-up and stable periods of platform development. Based on the results, we derive some insights and examine the cross-stage changes and commonalities. We provide both lessons learned from the past and practical implications for market managers and lenders in the current online lending market.

Adverse Selection in P2P Lending: Does Peer Screening Work Efficiently?—Empirical Evidence from a P2P Platform

The rapid development of online lending in the past decade, while providing convenience and efficiency, also generates large hidden credit risk for the financial system. Will removing financial intermediaries really provide more efficiency to the lending market? This paper used a large dataset with 251,887 loan listings from a pioneer P2P lending platform to investigate the efficiency of the credit-screening mechanism on the P2P lending platform. Our results showed the existence of a TYPE II error in the investors’ decision-making process, which indicated that the investors were predisposed to making inaccurate diagnoses of signals, and gravitated to borrowers with low creditworthiness while inadvertently screening out their counterparts with high creditworthiness. Due to the growing size of the fintech industry, this may pose a systematic risk to the financial system, necessitating regulators’ close attention. Since, investors can better diagnose soft signals, an effective and transparent enlargement of socially related soft information together with a comprehensive and independent credit bureau could mitigate adverse selection in a disintermediation environment.

Examining failure learning in online lending: Complete failure vs. incomplete failure

We examine the learning effects of borrowers’ failures in online lending. Based on funding ratios of borrowers’ loan listings in online lending, we first explore the role of failure degree in borrowers’ future funding performance. Further, we disaggregate borrowers’ funding failure into complete failure and incomplete failure, and compare theirs learning effects. Using a large sample of 610,000 online loan applications over six years from a Chinese leading online lending platform Renrendai, we use funding ratio to quantifiably measure each loan listing’s failure degree and conduct a series of tests. The results show that: (1) Borrowers’ failure degree of prior loan applications is negatively associated with one’s subsequent funding performance. (2) Borrowers’ complete failure cannot promote learning, while incomplete failure is good for future performance. (3) Both incomplete failure and complete failure interacted to influence the value of each type of experience and generate improved learning. Our results are robust across a variety of settings. The study sheds light for deeply understanding of failure learning phenomenon, and can also provide important implications for online lending managers to support successful financial transactions.

The Future Development Trend of P2P Online Lending Industry under The Financial Regulatory Policy and The New Situation

As an innovative Internet financial service mode, person to person (P2P) network lending has rapidly promoted the development of Inclusive Finance in China. However, in the development process of P2P network loan, there are also some problems, such as platform role deviation, institutional function alienation, industry credit difficulty and so on, which lead to a large number of phenomena, such as suspension of business, refund, difficulty in cash withdrawal and running away. Faced with this dilemma, the regulatory authorities began to carry out strict supervision on the industry and guide the benign exit and transformation of the industry. In view of this situation, this paper will predict the future development direction of China’s online lending combined with the financial regulatory policies and the future trend of Internet Finance under the new situation.

Exploring Loan Sharks in Online Peer-to-Peer Lending Applications: A Case Study of Vietnam

Online peer-to-peer lending applications have emerged in some recent years in Vietnam, where the consumer lending market is potential for financial companies. This country faces challenges to control the online lending business. As a result, criminals find loopholes in the legal system on this business to commit loan sharks. This article targets to explore the nature of the connection between a loan shark and online peer-to-peer lending applications through interviewing with police officials in their specific operation investigated. The findings highlight a close structure of criminal groups related to loan sharks via this lending platform and a comprehensive profile of borrowers. These findings suggest some recommendations for fighting against this crime effectively in the coming time.

Why segmentation matters: a Machine Learning approach for predicting loan defaults in the Peer-to-Peer (P2P) Financial Ecosystem

Peer-to-Peer (P2P) lending is an online lending process allowing individuals to obtain or concede loans without the interference of traditional financial intermediaries. It has grown quickly the last years, with some platforms reaching billions of dollars of loans in principal in a short amount of time. Since each loan is associated with the probability of loss due to a borrower's failure, this paper addresses the borrower's default prediction problem in the P2P financial ecosystem. The main assumption, which makes this study different from the available literature, is that borrowers sharing the same homeownership status display similar risk profile, thus a model per segment should be developed. We estimate the Probability of Default (PD) of a borrower by using Logistic Regression (LR) coupled with Weight of Evidence encoding. The features set is identified via the Sequential Feature Selection (SFS). We compare the forward against the backward SFS, in terms of the Area Under the Curve (AUC), and we choose the one that maximizes this statistic. Finally, we compare the results of the chosen LR approach against two other popular Machine Learning (ML) techniques: the k Nearest Neighbors (k-NN) and the Random Forest (RF).