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 (BIRTGS) 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