Enhancing Financial Service Systems Through OLAP and OLTP Integration

Current financial systems face daily challenges in managing large volume of data, involved in various types of transactions, which require rapid and precise handling. Traditional OLTP databases are designed for real-time transaction processing; however, as data volume increases, these systems experience challenges in performance, high resource consumption, and complex data validation, especially with concurrent operations across multiple services. This research aims to design and develop an approach to integrate deep data analysis with real-time transaction processing to alleviate performance constraints and enhance large-scale data management. This improvement increases the ability to analyze complex data, allows efficient and timely report generation, transaction monitoring, and in-depth data retrieval. Additionally, our research includes the development of a data modelling tool based on data models, enabling the users to manage and analyze data independently through an intuitive interface without requiring a technician. This application tool empowers the users to gain a comprehensive data overview, conduct in-depth analyses, and generate reports with ease. Our study and experiment show that using OLAP alongside with OLTP significantly enhances the ability to handle large and complex data without altering the existing OLTP structure. Our solution also empowers the user to perform data analysis in a self-driven manner, enhancing strategic decision-making in response to business demands.