Artificial Intelligence and Commercial Bank Operational Efficiency in the Fintech Era

Abstract

Artificial intelligence can accelerate information processing in commercial banks, but faster individual tasks do not necessarily produce lower operating costs or more efficient financial intermediation. This article develops a critical, integrative review of research on banking efficiency, financial technology, machine learning and generative artificial intelligence. It distinguishes evidence about prediction and workflow performance from estimates of bank-level efficiency and profitability. The synthesis identifies three principal channels: automating document-intensive operations, improving the allocation of screening and monitoring effort, and supporting employees in customer service and knowledge work. Evidence from digital lending supports processing gains, while bank-level studies suggest performance associations that remain sensitive to technology measurement and identification assumptions. Generative artificial intelligence offers additional opportunities, although influential productivity estimates come from settings outside banking. The article proposes an evaluation framework linking application-specific outcomes to full lifecycle costs, service quality and risk-adjusted bank performance. Data governance, complementary organisational investment and appropriate human oversight determine whether local productivity gains persist after implementation, validation and remediation costs. The central implication is that commercial banks should evaluate verified improvements in completed workflows rather than adoption counts or model accuracy alone. Further research requires deployment-level data, credible comparison groups and longer observation of credit and operational risks.