Simulating Data Stories of Clients’ Credit Card Default

Clients who have problems paying with their credit cards need to be identified. However, data analysis is rather difficult due to the numerical nature of raw data. In the digital era, it is necessary to deploy dashboards and visual items to assist users in obtaining information more quickly. Typically, the dashboard comprises relevant charts and graphics. In this study, a Sankey chart is recommended. The purpose of this project is to simulate the construction of a dashboard that supports users at credit agencies or financial institutions in detecting credit card payment defaults. User requirements, data pre-processing, exploratory data analysis (EDA), data treatment, modelling, and assessment are all part of the development process. It is recommended to use a Sankey chart to examine patterns based on delayed monthly patterns. The result of this study shows that the largest percentage (71%) of the clients who do not have default payments in the coming month are those who are using revolving credit, paying duly, or those who do not consume the credit card. The usability assessment yielded a 4.0 mean on a five-point Likert scale which indicates that respondents agree that the dashboard is usable. In future development, a prediction model will be created and included into the default payment indicator.