Analyzing the Importance of Electronic Data Capture (EDC) for Sales Volume Prediction at Bank Merchants with the Arima Method

Payments with Electronic Data Capture (EDC) are becoming increasingly attractive to banks due to their efficiency, convenience and potential for data-based insights. The study aims to explore the use of transaction data from debit and credit cards at one of the largest banks in Indonesia to predict following year’s sales volume and growth using the Autoregressive Integrated Moving Average (ARIMA) method. Historical data on debit card and credit card transactions based on a predictive model using the ARIMA method was generated. The purpose of this study was to forecast sales volumes for the following year and identify potential growth patterns. Data preprocessing, model selection, and evaluation to ensure robust and accurate predictions as the methodology is used. The output of this study in the form of predictions for the following year can be used by banks EDC machines issuers in designing business plans in order to increase sales transactions. For further research, the results of this study can be used to measure the gap between forecasting results and actual results.