User review analysis based on text mining analysis for financial app operating companies : Comparison of Financial Payment Apps and Financial Product Recommendation Apps

The recent proliferation of mobile devices and the impact of COVID-19 are driving financial services to become more online and mobile-centric, As the boundaries between financial and non-financial services have been blurred, user demands and complaints for additional financial app features have also increased. In this study, we classified financial apps into financial payment apps and financial product recommendation apps, and analyzed the review data to identify ways to improve financial apps. To this end, this study utilized the LDA topic modeling method to analyze topics by dividing them into two dimensions and derive improvement measures by dividing them into financial services, UI/UX design, and system aspects. The research findings indicate that, in terms of financial services, financial payment apps should focus on improvements centered on appropriately adjusting the number of advertisements, while from a system perspective, enhancements should be centered on addressing errors occurring during the simplified payment process. For financial product recommendation apps, improvements should be centered on mitigating the risk of user personal information leakage in the financial services aspect, adding a photo attachment feature to the household ledger function in the UI/UX design aspect, and resolving discrepancies in recommended card information in the system aspect. We believe that financial companies can develop and improve their financial apps by utilizing the improvement measures in this study.