Next Generation Financial Security: Leveraging AI for Fraud Detection, Compliance and Adaptive Risk Management
Keywords:
AI in Finance, Financial Fraud Detection, AI-driven Risk Management, AI Ethics, Cybersecurity in BankingAbstract
The fast-digital transformation of the financial sector has created unprecedented prospects for development and efficiency, while also increasing the complexity and size of security threats. This article thoroughly investigates the advances and issues connected with AI-driven financial security, with an emphasis on asset protection and risk management. The study uses a mixed-methods research strategy, combining quantitative benchmarking of advanced AI models against traditional rule-based systems with qualitative analysis of regulatory compliance and operational scalability. To assess model adaptability, robustness, and bias, the researchers use a hybrid dataset that includes 3 million structured transaction records, simulated fraud scenarios, and over 15,000 regulatory requirements. The results show that ensemble AI models outperform conventional systems in fraud detection, with high precision (0.93), recall (0.91), and adaptability while maintaining a sub-50 ms latency at scale. The paper also emphasizes the importance of explainable AI and adversarial debiasing in upholding ethical and regulatory standards. However, constraints remain, notably the difficulty of simulating zero-day attacks and ensuring worldwide generalizability. The findings highlight AI's transformative potential in financial security, while also underlining the importance of continuous model retraining, ethical monitoring, and regulatory engagement. The report finishes by highlighting gaps in current research and recommending future strategies for improving the resilience and trustworthiness of AI-driven financial systems.
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