Artificial Intelligence in Investment Banking Risk Management: Applications, Benefits, and Governance Challenges

Abstract

Artificial Intelligence (AI) is revolutionizing the investment banking landscape and its impact on risk management is undeniable, with the ability to analyze vast volumes of complex and heterogeneous data to make more informed and timely decisions regarding market risk, credit risk, operational risk, and compliance risk. The design of this study is qualitative, descriptive, and secondary data analysis, which is based solely on academic literature, industry reports, and regulatory publications, from which the synthesis of how AI tools are used in the different stages of the risk management process (identification, measurement, monitoring, and mitigation) is taken. Thematic content analysis is applied to identify the key areas of the common applications of AI: short-term market forecasts, AI-enhanced credit scoring, anomaly-based fraud detection, and natural language processing (NLP) for surveillance, and the perceived benefits and implementation challenges. The results show that AI can have a substantial impact on predictive accuracy, early identification of anomalies and unethical activity, operational efficiency, and integration of multiple data sources, which can add to the resilience of banks when implemented in a strong governance structure. Meanwhile, the growing complexity of models, data quality challenges, lack of model explainability, potential model bias, and shifting regulations expectations add to a new layer of model and governance risk that needs to be managed. The study concludes that AI should be considered a useful complementary tool, not a replacement, for the traditional quantitative models and investment bankers' experience, and recommends that investment banks have clear AI strategies, improve model risk management, invest in cross-functional capabilities and integrate ethical and regulatory aspects throughout the AI lifecycle.