Architecting Scalable and Resilient Fintech Platforms with AI/ML Integration

The rise of Fintech brought about new dimensions of scaling, tweaks to the regulatory landscape, as well as the advent of AI and machine learning in financial technology. Alongside the growth in technology and the human obsession with fast operating speeds, the building of large-scale platforms that can undertake a range of regulatory compliance checks and high transactions per second is high stake. This paper takes a closer look at the design principles and infrastructure backend strategies necessary for the development of a platform for Fintech capable of these huge throughputs as well as maneuverability at the hands of regulatory complexity and dynamically changing markets. It will go from the analysis of the microservices useful for AI and ML applications in detecting fraud through risk modeling to engaging customers; data engineering pipelines and cloud-native propositions indirectly used for AI architecture would then be studied. Here, the paper comes under an abstracted cloud, black of concrete architectures that some authors are still shy of mentioning.