Research on the Construction of Financial Services Transaction Data Analysis and Mining Model

With the deepening development of the global financial market, financial services trading activities are becoming increasingly frequent and complex. Especially under the influence of digital economy, the amount of data generated by online financial transactions has exploded. How to effectively manage and use these data has become the key for financial institutions to enhance market competitiveness and prevent financial risks. In this regard, based on the actual application requirements, this paper will propose a set of fraud detection model combined with data mining technology, aiming at strengthening the mining and analysis ability of financial service transaction data and promoting the further improvement of the risk assessment management system of financial institutions. Practice has proved that after the clustering analysis of merchant transaction characteristics is completed by K-means algorithm, the fraud detection model is constructed by using logistic regression algorithm, which can effectively mine and analyze financial service transaction data, thus improving the efficiency and quality of merchant fraud detection and providing necessary technical support for risk management of financial institutions.