Event-Based Dynamic Banking Network Exploration for Economic Anomaly Detection

TL;DR

This paper introduces a novel method for detecting financial instability by analyzing dynamic banking transaction networks and identifying specific triadic motif patterns associated with instability periods.

Contribution

It proposes a new approach using network motif analysis to detect financial instability, focusing on micro-level network patterns during major events.

Findings

Identified a specific triadic motif pattern linked to financial instability.

Demonstrated the method's effectiveness during the Eid al-Fitr period in Indonesia.

Supports enhanced supervision of financial system stability.

Abstract

The instability of financial system issues might trigger a bank failure, evoke spillovers, and generate contagion effects which negatively impacted the financial system, ultimately on the economy. This phenomenon is the result of the highly interconnected banking transaction. The banking transactions network is considered as a financial architecture backbone. The strong interconnectedness between banks escalates contagion disruption spreading over the banking network and trigger the entire system collapse. This far, the financial instability is generally detected using macro approach mainly the uncontrolled transaction deficits amount and unpaid foreign debt. This research proposes financial instability detection in another point of view, through the macro view where the banking network structure are explored globally and micro view where focuses on the detailed network patterns called motif. Network triadic motif patterns used as a denomination to detect financial instability. The most related network triadic motif changes related to the instability period are determined as a detector. We explore the banking network behavior under financial instability phenomenon along with the major religious event in Indonesia, Eid al-Fitr. We discover one motif pattern as the financial instability underlying detector. This research helps to support the financial system stability supervision.