Financial institutions face many challenges in managing modern financial transactions and vast data volumes. To overcome these challenges, they are increasingly harnessing advanced data man- agement technologies such as artificial intelligence and cloud computing. This paper presents a com- prehensive review of how these tools transform financial decision-making in various domains and ap- plications. We analyzed both foundational and recent advancements using a rigorous methodology based on the PRISMA 2020 guideline. Our findings indicate that many major financial institutions are adopting AI-driven solutions to potentially enhance real-time risk assessment, transactional efficiency, and predictive analytics. While they bring benefits like faster decision-making and reduced operational costs, they also pose challenges like data security and integration complexities that require further research and development. Looking ahead, we envision a more integrated, responsive, and secure financial ecosystem that leverages the convergence of AI, cloud computing, and advanced data storage. This synthesis underscores the significance of contemporary data management solutions in shaping the future of data-driven financial services, offering a guideline for stakeholders in this evolving domain.

Modern financial institutions now manage increasingly advanced data-related activities and place a growing emphasis on environmental and energy impacts. In financial modeling, relational databases, big data systems, and the cloud are integrated, taking into consideration resource optimization and sustainable computing. We suggest a four-layer architecture to address financial data processing issues. The layers of our design are for data sources, data integration, processing, and storage. Data ingestion processes market feeds, transaction records, and customer data. Real-time data are captured by Kafka and transformed by Extract-Transform-Load (ETL) pipelines. The processing layer is composed of Apache Spark for real-time data analysis, Hadoop for batch processing, and an Machine Learning (ML) infrastructure that supports predictive modeling. In order to optimize access patterns, the storage layer includes various data layer components. The test results indicate that the processing of market data in real-time, compliance reporting, risk evaluations, and customer analyses can be conducted in fulfillment of environmental sustainability goals. The metrics from the test deployment support the implementation strategies and technical specifications of the architectural components. We also looked at integration models and data flow improvements, with applications in finance. This study aims to enhance enterprise data architecture in the financial context and includes guidance on modernizing data infrastructure.

Today, financial institutions’ architecture does not rely on one single technology. Instead, it uses a multi-technology approach in order to cover modern requirements and, at the same time, remain relevant. It integrates technologies such as relational databases, Big Data for analysis, and Cloud environments for distributed capacities within a complex data architecture. At the same time, due to European data governance regulations, governance mechanisms such as encryption, pseudonymization, and incremental versioning must be applied on each architectural layer in order to comply with strict European governance rules. In this study, the impact of data governance is assessed by applying these mechanisms from the data-ingestion level, using diverse data types such as structured, semi-structured, and unstructured data, across relational databases, Big Data analysis, and Cloud distributed systems. In doing so, metrics such as execution time, CPU, and memory usage are assessed in order to properly evaluate the impact of governance mechanisms on financial systems. The results show that governance can be successfully integrated, provided these mechanisms are embedded at the architectural level, ensuring that performance, scalability, and compliance are maintained across the entire processing pipeline.

Today, national and international information and news have greatly increased in volume. Almost all people have access to everything they want through devices and the internet. Information is just a click away. But besides this very good aspect, there are also negative sides. A negative aspect, but also the most important, is the fact that news can be distorted by those who process it and post it online. Thus, you can read information, but it may not be the real one, thus becoming misinformed. Various researches have been carried out that have tried to discover methods and algorithms to detect this fake news, but not all algorithms used are effective in detecting whether online content is false or true. There are machine learning algorithms that can detect such news and tell whether the information is true or false. This paper aims to discover which supervised learning algorithm is the most efficient and best at detecting fake news from real ones. Thus, several supervised learning algorithms were trained on a dataset taken from Kaggle and finally the most efficient algorithm that can be used for fake news detection will be presented.