Artificial Intelligence Adoption and Financial Stability under Geopolitical Pressure: Evidence from Indonesia’s Digital Banking Sector

Anniza Citra  Prajasari

doi:10.14421/jbmib.2025.0402-01

Authors

Anniza Citra Prajasari Doctoral Program in Islamic Economics and Halal Industry, Universitas Gadjah Mada, Yogyakarta, Indonesia

DOI:

https://doi.org/10.14421/jbmib.2025.0402-01

Keywords:

Artificial Intelligence, Geopolitical Risk, Financial Stability, Digital Banking, Sensitivity Analysis

Abstract

Research Aims: This study investigates how Artificial Intelligence (AI) adoption and Geopolitical Risk Index (GPR) influence the financial stability of Indonesia’s digital banking sector, focusing on profitability (ROA) and credit risk (NPL).

Design/methodology/approach: Using annual panel data from 2021–2024 and employing regression and scenario-based simulations to evaluates both structural effects and conditional responses to varying GPR levels.

Research Findings: The findings reveal that higher AI adoption generally enhances profitability and reduces credit risk under low to moderate geopolitical risk. However, AI’s influence remains statistically insignificant, while GPR significantly decreases NPL, indicating conservative lending behaviorduring uncertainty. Operational efficiency and capital adequacy are identified as key internal factors influencing profitability.

Theoretical Contribution/Originality: This study contributes to the understanding of digital banking resilience by integrating econometric and simulation techniques, providing policy insights that emphasize adaptive credit risk frameworks, AI-driven risk management, and capital buffer adjustments amid geopolitical volatility.

Research limitation and implication: These findings imply that digital banks should prioritize strengthening operational efficiency and capital buffers, while leveraging AI adoption and GPR monitoring as supportive tools to mitigate potential pressures on profitability and credit quality.

This research model can be recalibrated using GPR data to predict NPL spikes and ROA decline.

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