Due to the ever-increasing adoption of AI systems in the financial space, it is necessary to assess these regulatory frameworks, such as IEC 62304 and PCI DSS. As AI technologies within the finance sector process huge quantities of data that are sensitive, like transaction and personal information, these must be handled securely so that these are not breached or involve fraud—meeting the strict data security standards, privacy, and operation standards for a medical device software compliance with IEC 62304 and PCI DSS for payment card data security results. This article investigates how these compliance frameworks create the responsibility for designing, structuring, and building AI systems in financial institutions. It describes the technical problems in implementing real-time financial data processing and the issues addressed with cloud-native platforms, encryption, and data management applications. It discusses how, with technological advancements like large language models, Apache Kafka, and Apache Spark, the resulting financial AI systems can be compliance-driven and perform well. The article also delves into the ethical options of AI in finance and, in particular, data privacy, bias, and transparency. The conclusions include insights into the future of AI compliance with new technologies such as quantum computing and blockchain that will change the face of science. This study offers an actionable roadmap for companies to address the difficulties of regulatory compliance in the vein of AI’s potential fulfillment.
