Year: 2025
Type: article
Abstract: Due to the rapid development of artificial intelligence (AI), banking and financial services have had a significant impact on financial and operational risk management. With the use of AI-powered technologies such as machine learning, deep learning, and natural language processing, financial institutions have been able to help improve their predictive capabilities, make decisions faster, and reduce risks better. This paper aims to provide a critical analysis of how AI is playing out in financial and operational risk management based on the theoretical foundations, applications, tools, ethical considerations, and legal implications, as well as the future trajectories of AI in the financial and operational risk management space. This study examines the possibilities of AI-driven solutions in predictive analytics, fraud detection, credit risk assessment, algorithmic trading, compliance monitoring, and operational risk management. AI systems can use large datasets to identify patterns, identify anomalies, and make data-driven predictions so they can enhance the accuracy of risk assessment and financial decisions by leveraging data. Furthermore, AI helps optimize the portfolio in a dynamic condition as the portfolio is updated based on the actual time conditions of the market. Yet, despite its potential for transformational impacts, AI also presents some ethical and legal challenges — data privacy, algorithmic bias, a lack of transparency, issues of regulatory compliance, and cybersecurity threats, among them. This paper shows the practical utilization of AI in financial institutes through real-world situations and the impact of AI on mitigating risk and enhancing efficiency and security. The study implies that AI would remain the critical factor in the development of financial risk management, turning out to be a nice thing for innovation and defending in a more intricate career. However, the study demonstrates that responsible deployment and long-term sustainability in financial operations depend, among other things, on regulatory oversight, ethical AI governance, and human-AI collaboration.
Author: Olukunle Ogundimu
Institution: Nexford University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4409197227