Artificial intelligence in banking: a transformative force for profitability or an overhyped investment? Evidence from Malaysia

Purpose The study aims to examine the impact of artificial intelligence (AI) adoption on bank profitability in Malaysia, focusing on challenges traditional banks face in AI-driven competition. Grounded in the resource-based view (RBV) and dynamic capabilities theory (DCT), it examines AI as a strategic asset that links to financial performance and operational transformation. Design/methodology/approach Using data from 32 Malaysian banks (2019–2023), the study applies a 2-step system generalised method of moments (GMM) estimator and quantile regression (QR) to examine AI adoption measured via an AI index and its impact on return on assets (ROA), return on equity (ROE) and cost-to-income ratio. Findings AI adoption is significantly linked to higher ROA and ROE, but its impact on cost efficiency remains inconclusive due to integration costs and legacy constraints. QR shows cost efficiencies are more evident in high-CIR banks. Research limitations/implications The study focuses on 32 Malaysian banks, which may limit generalisability, and the 5-year period may not capture long-term AI effects. Practical implications Banks should invest in AI, modernise legacy systems and build an AI-driven culture. Staff training, risk management and agile governance are key to maximising AI financial benefits. Policymakers must balance innovation and compliance, as AI adoption boosts financial inclusion, stability and growth through digital banking. Originality/value The study extends extant literature by employing RBV and DCT to explain AI adoption and it also introduces a novel AI index for measuring adoption levels.