RTGS DETERMINANT & SUB-TRANSACTION BEHAVIOR DURING COVID-19 IN INDONESIA

Abstract

This paper investigates external factors affecting Indonesia’s Real Time Gross Settlement (RTGS) transactions by applying machine learning regularization to identify key variables from a large dataset. A Vector AutoRegression (VAR) model analyzes dynamic links among RTGS sub-transactions, while Impulse Response Function (IRF) analysis examines system behavior during COVID-19 shocks. Using monthly data on 75 economic indicators, we show that 21 variables most accurately capture the movement of Bank Indonesia Real Time Gross Settlement System (BI-RTGS) transactions. The study shows that monetary operation sub-transactions most strongly affect other BI-RTGS sub-transactions. Impulse Response Function analysis also finds that shocks in customer transfers and capital market transactions during COVID-19 can negatively impact fiscal soundness and financial stability.