authors
Himanshu Jagwani, Khushbu Jagwani and Sukhjeet Kaur Matharu

abstract
Simultaneously, generative artificial intelligence (AI) is quickly changing the nature of financial crime, making it possible to engage in new fraudulent, identity-related, cyberattacks, and automated money-laundering practices. This paper explores the way that financial criminals are taking advantage of generative AI technologies and studies the inferences of the risk management and detection systems of banks. The article evaluates 2,840 confirmed AI-enabled fraud attempts, 612 million operational losses, and the effectiveness of 18 frequently implemented countermeasures using incident data gathered on financial institutions in 2021-2025. A combined quantitative-based incident analysis, survey data analysis of 54 banks, and qualitative data analysis based on fraud investigation teams has shown that the number of generative-AI-driven impersonation schemes increased by 263 percent, synthetic identity attacks by 188 percent, and AI-based social-engineering attacks by 310 percent over the period of five years. The successful loss of fraud was reduced by an average of 37% in banks that implemented AI-native detection models and at a considerably higher rate in banks that largely depended on rules-based detection. The research has its theoretical and empirical value as it provides a single conceptual model of AI-facilitated financial crime, outlines the essential loopholes in bank readiness, and analyzes the relative efficiency of security measures. The results highlight the fact that it is high time to implement AI-enhanced fraud detection, improve governance systems, and have a coordinated regulatory reaction throughout the financial ecosystem all over the world.