This research delves into the optimization of payment gateway systems in the fintech industry using AI-augmented OCR and intelligent workflow automation, especially with a focus on Azure Functions. The paper explores how these tools can make payment processing more efficient, scalable, and cost-effective by combining serverless computing with AI-driven technologies. Cases from financial institutions adopting Azure services, which indicate KPIs for transaction speed, operational efficiency, and total cost of ownership. Surveys from IT experts and system architects give real-world insight into how AI and OCR apply in the payments system.The event-driven architecture of Azure Functions features AI-powered Optical Character Recognition (OCR) for the automatic processing of payment information and intelligent workflows optimizing decision making. It would improve processing time, eliminate human errors, and make transactions' data accuracy more efficient. In addition, features such as Azure Active Directory, encryption, and compliance with PCI DSS standards ensure the safe handling of sensitive payment data, thereby enhancing the security of these systems.The paper compares AI-augmented OCR and intelligent workflows with traditional server-based models with respect to metrics involving transaction speed, system scalability, and cost-effectiveness. Preliminary findings suggest that AI-and OCR-based integration with Azure Functions reduces costs, enhances scalability, and strengthens security in payment gateway systems. These technologies bring about a revolution in payment processing in fintech and improve the efficiency and save operational expenses.

Today, in the accelerating pace of cyber threats, new approaches and adaptive protection are required, specifically on risk analysis and fraud. Therefore, Adversarial Machine Learning with Artificial Intelligence provides a complete framework for fast-in-time cybersecurity threats assessment and advanced fraud detection capability. AML techniques would be critical for representing potential online attacks, ensuring AI-enabled defense systems are stronger and more resilient, and ultimately mitigating an adversarial attack. The fact that AML has both defensive and attacking uses shows that it's the requirement for developing models resistant to adversaries that is being made with a proper defense against emerging cyber threats. It introduces an adaptive risk identification framework, using predictive modeling, machine learning algorithms, and real-time data analysis for detecting, ranking, and mitigating risks dynamically. This is the method that keeps security protocols one step ahead of possible threats-they make this happen through a continuous evolution in their capacities to keep up with the speed of the digital world. Examples of using AI as one of the impressive tools in analyzing and owning huge datasets to detect anomalies and discover patterns of fraudulent behavior are, among others, the use of AI to do online fraud detection. It shows practical use of AI for fraud detection, from secure online authentication to preventing financial fraud. Evidence from real life helps demonstrate how AI-driven solutions can improve current risk assessment methodologies and fraud detection systems in response to increasingly complex cyber threats. The results provide a holistic view of how AML and AI techniques strengthen cyber defenses while contributing to effective cybersecurity practices. The demand for AI to take up mobility in AML has, however, begun to be very pertinent regarding the safety of digital assets, integrity of online systems, and the walls of defense erected against increasingly sophisticated cyber threats in a highly connected digital terrain. This study reflects the critical adoption of intelligent and adaptive solutions in meeting the demands posed by modern cybersecurity.