Artificial Intelligence and Financial Modernization: Navigating the Security-Innovation Paradox in Contemporary Banking Systems

Abstract

The introduction of artificial intelligence into the world of finance is a big change. It alters how banks work, how transactions are handled, and how customers are engaged. This article looks at the tricky situation between tech improvements and the need for security in today's finance. There's a strange situation where getting better tech also means facing new dangers. The progress from legacy and rule-based systems to advanced machine learning architectures has made financial institutions achieve unprecedented enhancements in fraud prevention, risk evaluation, and business processes. The advancements also introduce new attack surfaces, such as adversarial manipulation, data poisoning, and model extraction attacks that traditional security paradigms are ill-equipped to deal with. By conducting extensive evaluation of present-day deployment trends, future threats, and defensive standpoints, this article illustrates that effective AI integration also necessitates an essential reconceptualization of security paradigms. The discourse includes multi-layered defense systems with differential privacy, federated learning approach, and explainable AI methods while ensuring strategic human control. Financial institutions face upcoming challenges, particularly for cybersecurity and data privacy, due to quantum computing-led disruptions and a shifting regulatory landscape, which they should handle preemptively. This means balancing security with new ideas by constantly testing concepts, creating strong management systems, and thinking about how these changes affect society, like market dominance and protecting the environment.