| dc.description.abstract | Fintech globally continues to experience an increase in 2020 which the fintech investment reaching USD 48,8 billion, and in 2021 the third quarter will double to USD 94,7 billion. The problem faced by P2P companies is that during the pandemic, the value of Non-Performing Loans (NPL) for P2P touched the highest in 2020, reaching 8,8%. In 2021, even though the NPL is in good condition, there is a fluctuating pattern. In the profitability aspect, after the pandemic, P2PL profitability increased dramatically from -7,36% in early 2021 to the highest recorded at 10.85%. This high level of profitability has had a very significant effect on fintech growth from 2018 to 2021. The dynamics and developments in the P2PL fintech industry are interesting for deeper analysis. In this study, the aims were to map the P2PL fintech industry, find out the determinants of ROE, and determine the determinants of NPL.
In this study, the data used came from surveys to each of the P2PL fintech companies to obtain independent variable data. The analytical method used is the ordinal logistic regression method to determine the relationship between variables. The data collected is from 2019 to 2021. From the results of the hierarchical clustering mapping, there are three companies in cluster 1 where this company has the best performance, namely the highest disbursement and low NPL. In cluster two, there are 77 companies with disbursement below the total disbursement average and NPL below the total NPL average. While in cluster 3, there are 14 companies with high NPL values and low disbursement. Based on the results of ordinal logistic regression analysis, there are 11 independent variables that affect ROE, monthly active user, Operational Costs on Operational Revenues ratio (BOO), the use of biometrics, the use of robotic process automation, the use of SLA, integrated platform, distribution time, delivery of accurate information, the use of social media, the use of features chat on the application, and the type of credit scoring used. Based on the results of ordinal logistic regression analysis for NPL, there are 4 independent variables that influence NPL, the use of biometrics in lending, the use of digital marketing in promotions, the use of digital signatures in lending, and the use of social media.
From the results of this study, there are several policy implications. The performance mapping dominated only some companies with high disbursement and good NPL value. These results show that the P2P lending industry needs to be more mature. The results of this study can be used as a reference for the government to maintain the fintech lending ecosystem. The ease of obtaining credit insurance, the vacation of licensing, and encouraging investment to increase technological innovation must be carried out so that the business climate for fintech lending remains good. The government's role as a supervisor is also very instrumental in determining the success of the fintech lending business line. | id |
