Artificial Intelligence in Investment Banking Operations: A Study on Efficiency, Risk Management, and Strategic Decision-Making

This study empirically investigates the impact of Artificial Intelligence (AI) adoption on three critical performance dimensions in investment banking: operational efficiency, risk management effectiveness, and strategic decision-making quality. Despite the accelerating integration of AI across global financial institutions, structw'ed empirical evidence isolating AI's impact within investment banking -using large­ scale, multi-institutional primary data-remains limited. This study addresses the gap through a cross­ sectional quantitative survey of239 investment banking and financial analytics professionals in Bangalore's Global Capability Centres (GCCs) and affiliated institutions. Grounded in the Technology Acceptance Model (TAM), Resource-Based View (RBV), and Decision Theoly, the study tests five hypotheses using Pearson correlation analysis, one-sample t-tests, and one-way ANOVA All five hypotheses are supported at p < 0.001. AI adoption exerts its strongest positive impact on operational efficiency (r = 0.929), followed by risk management effectiveness (r = 0.926) and strategic decision-making quality (r = 0.850). Fraud detection, regulatory compliance, and workflow automation are the highest-impact application domains. Implementation challenges -particularly data privacy concerns and legacy sys1em integration- exert a significant negative moderating effect (r = --Q.618). Institutional heterogeneity is pronounced: FinTech companies and global investment banks substantially out perform domestic banks in AI­ driven operational outcomes (F = 136.678, p < 0.001). The study extends TAM,RBV, and Decision Theory to the investment banking context andprovidesactionab1e strategic guidance for f"mancial institutions, regulators, and AI solution providers