Big Data-Driven Financial Risk Management: Application of Association Rule Mining Technology in Anomaly Detection

With the rapid advancement of the informatization process, corporate information management has received increasing attention. Facing the increasingly complex and volatile socio-economic environment, the difficulty of enterprise risk management has also grown progressively. Establishing an efficient risk management mechanism for corporate risk early-warning is the goal enterprises seek to achieve. Identifying useful information for financial risk early-warning management from the vast amount of financial data generated by business operations is an urgent issue that enterprises currently need to resolve. The continuous improvements and innovations in big data and data mining technologies, as well as their excellent performance in the analysis of massive datasets, have brought them closely together. Therefore, this paper uses data mining technology to study and analyze corporate financial risk early-warning and to conduct anomaly detection on financial risks, which has great theoretical and practical significance. This paper proposes a more efficient P-Apriori mining algorithm based on the parallel processing of frequent itemsets groupings. This algorithm first divides the frequent (k-1)-itemsets into groups using certain rules. After grouping, each set of frequent (k-1)-item subsets can not only be processed in parallel but can also significantly reduce the self-joining times of frequent (k-1)-itemsets. Then, by combining the frequent k-item subgroups produced by each group, the frequent k-itemsets can be obtained. This effectively reduces the time required to generate frequent itemsets and improves the efficiency of mining, providing technical support for further application research on financial risk analysis.