Transforming Financial Risk Management and Operational Efficiency: The Impact of AI Adoption in Indonesian Banks

Abstract

This study investigates the impact of Artificial Intelligence (AI) adoption on operational efficiency and financial risk management in six major Indonesian banks: BNI, BRI, BCA, Danamon, Mandiri, and CIMB Niaga. The research implements Difference-in-Difference (DiD) methodology coupled with Bayesian Vector Autoregression (BVAR) Scenario Testing to assess how AI affects financial indicators comprising NPLs, ROA, CAR, and LDR. The study shows that AI adoption yields minor, immediate results but does not produce substantial improvements in financial performance or risk management when moving beyond Level 3. AI improves operational performance by enabling automated processes and optimised decisionmaking, leading financial institutions to enhance their fraud detection, credit risk management, and liquidity risk management. The study demonstrates the value of implementing AI through an orderly strategic plan while mandating that organisations establish rules and ethical guidelines for AI use throughout its adaptation stages. The implementation of AI by Indonesian banks offers multiple prospects to enhance their operational performance, risk management capabilities, and financial stability over the years.